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Controllability of structural brain networks

Author

Listed:
  • Shi Gu

    (University of Pennsylvania
    University of Pennsylvania)

  • Fabio Pasqualetti

    (University of California)

  • Matthew Cieslak

    (University of California)

  • Qawi K. Telesford

    (University of Pennsylvania
    Translational Neuroscience Branch, Army Research Laboratory)

  • Alfred B. Yu

    (Translational Neuroscience Branch, Army Research Laboratory)

  • Ari E. Kahn

    (University of Pennsylvania)

  • John D. Medaglia

    (University of Pennsylvania)

  • Jean M. Vettel

    (University of California
    Translational Neuroscience Branch, Army Research Laboratory)

  • Michael B. Miller

    (University of California)

  • Scott T. Grafton

    (University of California)

  • Danielle S. Bassett

    (University of Pennsylvania
    University of Pennsylvania)

Abstract

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Suggested Citation

  • Shi Gu & Fabio Pasqualetti & Matthew Cieslak & Qawi K. Telesford & Alfred B. Yu & Ari E. Kahn & John D. Medaglia & Jean M. Vettel & Michael B. Miller & Scott T. Grafton & Danielle S. Bassett, 2015. "Controllability of structural brain networks," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9414
    DOI: 10.1038/ncomms9414
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    Cited by:

    1. Zurrin, Riley & Wong, Samantha Tze Sum & Roes, Meighen M. & Percival, Chantal M. & Chinchani, Abhijit & Arreaza, Leo & Kusi, Mavis & Momeni, Ava & Rasheed, Maiya & Mo, Zhaoyi & Goghari, Vina M. & Wood, 2024. "Functional brain networks involved in the Raven's standard progressive matrices task and their relation to theories of fluid intelligence," Intelligence, Elsevier, vol. 103(C).
    2. Tadić, Bosiljka & Chutani, Malayaja & Gupte, Neelima, 2022. "Multiscale fractality in partial phase synchronisation on simplicial complexes around brain hubs," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Bruton, Oliver J., 2021. "Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron," Intelligence, Elsevier, vol. 86(C).
    4. Maria Isabel García-Planas & Maria Victoria García-Camba, 2022. "Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach," Mathematics, MDPI, vol. 10(3), pages 1-13, January.
    5. Elizabeth L. Johnson & Jack J. Lin & David King-Stephens & Peter B. Weber & Kenneth D. Laxer & Ignacio Saez & Fady Girgis & Mark D’Esposito & Robert T. Knight & David Badre, 2023. "A rapid theta network mechanism for flexible information encoding," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Dian Lyu & Shruti Naik & David K. Menon & Emmanuel A. Stamatakis, 2022. "Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Dhruv Saxena & Alexis Arnaudon & Oscar Cipolato & Michele Gaio & Alain Quentel & Sophia Yaliraki & Dario Pisignano & Andrea Camposeo & Mauricio Barahona & Riccardo Sapienza, 2022. "Sensitivity and spectral control of network lasers," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    8. Richard F Betzel & Katherine C Wood & Christopher Angeloni & Maria Neimark Geffen & Danielle S Bassett, 2019. "Stability of spontaneous, correlated activity in mouse auditory cortex," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    9. Ociepka, Michał & Kałamała, Patrycja & Chuderski, Adam, 2022. "High individual alpha frequency brains run fast, but it does not make them smart," Intelligence, Elsevier, vol. 92(C).
    10. Guilherme Ramos & Sérgio Pequito, 2020. "Generating complex networks with time-to-control communities," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-12, August.
    11. Atsushi Kikumoto & Apoorva Bhandari & Kazuhisa Shibata & David Badre, 2024. "A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. Huili Sun & Rongtao Jiang & Wei Dai & Alexander J. Dufford & Stephanie Noble & Marisa N. Spann & Shi Gu & Dustin Scheinost, 2023. "Network controllability of structural connectomes in the neonatal brain," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. S. Parker Singleton & Andrea I. Luppi & Robin L. Carhart-Harris & Josephine Cruzat & Leor Roseman & David J. Nutt & Gustavo Deco & Morten L. Kringelbach & Emmanuel A. Stamatakis & Amy Kuceyeski, 2022. "Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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