IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003557.html
   My bibliography  Save this article

Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity

Author

Listed:
  • David Samu
  • Anil K Seth
  • Thomas Nowotny

Abstract

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain.Author Summary: Macroscopic regions in the grey matter of the human brain are intricately connected by white-matter pathways, forming the extremely complex network of the brain. Analysing this brain network may provide us insights on how anatomy enables brain function and, ultimately, cognition and consciousness. Various important principles of organization have indeed been consistently identified in the brain's structural connectivity, such as a small-world and modular architecture. However, it is currently unclear which of these principles are functionally relevant, and which are merely the consequence of more basic constraints of the brain, such as its three-dimensional spatial embedding into the limited volume of the skull or the high metabolic cost of long-range connections. In this paper, we model what aspects of the structural organization of the brain are affected by its wiring constraints by assessing how far these aspects are preserved in brain-like networks with varying spatial wiring constraints. We find that all investigated features of brain organization also appear in spatially constrained networks, but we also discover that several of the features are more pronounced in the brain than its wiring constraints alone would necessitate. These findings suggest the functional relevance of the ‘over-expressed’ properties of brain architecture.

Suggested Citation

  • David Samu & Anil K Seth & Thomas Nowotny, 2014. "Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-24, April.
  • Handle: RePEc:plo:pcbi00:1003557
    DOI: 10.1371/journal.pcbi.1003557
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003557
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003557&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003557?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jesús Gómez-Gardeñes & Gorka Zamora-López & Yamir Moreno & Alex Arenas, 2010. "From Modular to Centralized Organization of Synchronization in Functional Areas of the Cat Cerebral Cortex," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-11, August.
    2. Murray Shanahan & Mark Wildie, 2012. "Knotty-Centrality: Finding the Connective Core of a Complex Network," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-7, May.
    3. David C. Van Essen, 1997. "A tension-based theory of morphogenesis and compact wiring in the central nervous system," Nature, Nature, vol. 385(6614), pages 313-318, January.
    4. Mark D Humphries & Kevin Gurney, 2008. "Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence," PLOS ONE, Public Library of Science, vol. 3(4), pages 1-10, April.
    5. Manfred G Kitzbichler & Marie L Smith & Søren R Christensen & Ed Bullmore, 2009. "Broadband Criticality of Human Brain Network Synchronization," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
    6. Meila, Marina, 2007. "Comparing clusterings--an information based distance," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 873-895, May.
    7. Marcus Kaiser & Claus C Hilgetag, 2006. "Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
    8. Ahn, Yong-Yeol & Jeong, Hawoong & Kim, Beom Jun, 2006. "Wiring cost in the organization of a biological neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 531-537.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pirker-Díaz, Paula & Díaz-Guilera, Albert & Soriano, Jordi, 2024. "Self-regulation of a network of Kuramoto oscillators," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    2. Yuhan Chen & Shengjun Wang & Claus C Hilgetag & Changsong Zhou, 2017. "Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-37, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ashish Raj & Yu-hsien Chen, 2011. "The Wiring Economy Principle: Connectivity Determines Anatomy in the Human Brain," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    2. Logan Harriger & Martijn P van den Heuvel & Olaf Sporns, 2012. "Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    3. Frank Emmert-Streib, 2013. "Structural Properties and Complexity of a New Network Class: Collatz Step Graphs," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-14, February.
    4. Julian M L Budd & Krisztina Kovács & Alex S Ferecskó & Péter Buzás & Ulf T Eysel & Zoltán F Kisvárday, 2010. "Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-25, March.
    5. Zueva Marina V, 2018. "A New Look at Stimulation Therapy with Complex-Structured Stimuli in Traumatic Brain Injuries," Global Journal of Addiction & Rehabilitation Medicine, Juniper Publishers Inc., vol. 5(1), pages 12-16, January.
    6. Francisco J. Valverde-Albacete & Carmen Peláez-Moreno, 2024. "A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets," Mathematics, MDPI, vol. 12(2), pages 1-31, January.
    7. Assaf Almog & Ferry Besamusca & Mel MacMahon & Diego Garlaschelli, 2015. "Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    8. Werner, Gerhard, 2013. "Consciousness viewed in the framework of brain phase space dynamics, criticality, and the Renormalization Group," Chaos, Solitons & Fractals, Elsevier, vol. 55(C), pages 3-12.
    9. Robert G. Sacco, 2019. "The Predictability of Synchronicity Experience: Results from a Survey of Jungian Analysts," International Journal of Psychological Studies, Canadian Center of Science and Education, vol. 11(3), pages 1-46, September.
    10. Wang, Qingyun & Zheng, Yanhong & Ma, Jun, 2013. "Cooperative dynamics in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 19-27.
    11. Andrea Avena-Koenigsberger & Xiaoran Yan & Artemy Kolchinsky & Martijn P van den Heuvel & Patric Hagmann & Olaf Sporns, 2019. "A spectrum of routing strategies for brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    12. Mark D Humphries & Javier A Caballero & Mat Evans & Silvia Maggi & Abhinav Singh, 2021. "Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-22, July.
    13. Huaylla, Claudia A. & Kuperman, Marcelo N. & Garibaldi, Lucas A., 2024. "Comparison of two statistical measures of complexity applied to ecological bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    14. Christian Meisel & Alexander Storch & Susanne Hallmeyer-Elgner & Ed Bullmore & Thilo Gross, 2012. "Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-8, January.
    15. Juan Lucio & Raúl Mínguez & Asier Minondo & Francisco Requena, 2016. "Networks and the Dynamics of Firms' Export Portfolio: Evidence for Mexico," The World Economy, Wiley Blackwell, vol. 39(5), pages 708-736, May.
    16. Ahmadi, Negar & Pei, Yulong & Pechenizkiy, Mykola, 2019. "Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 289-308.
    17. Capitán, José A. & Aguirre, Jacobo & Manrubia, Susanna, 2015. "Dynamical community structure of populations evolving on genotype networks," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 99-106.
    18. Alessandra Griffa & Mathieu Mach & Julien Dedelley & Daniel Gutierrez-Barragan & Alessandro Gozzi & Gilles Allali & Joanes Grandjean & Dimitri Ville & Enrico Amico, 2023. "Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    19. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    20. Todd Zorick & Mark A Mandelkern, 2013. "Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003557. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.