IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41499-w.html
   My bibliography  Save this article

Network controllability of structural connectomes in the neonatal brain

Author

Listed:
  • Huili Sun

    (Yale University)

  • Rongtao Jiang

    (Yale School of Medicine)

  • Wei Dai

    (Yale School of Public Health)

  • Alexander J. Dufford

    (Oregon Health & Science University)

  • Stephanie Noble

    (Northeastern University
    Northeastern University
    Northeastern University)

  • Marisa N. Spann

    (Vagelos College of Physicians and Surgeons, Columbia University
    New York State Psychiatric Institute)

  • Shi Gu

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China)

  • Dustin Scheinost

    (Yale University
    Yale School of Medicine
    Yale University
    Yale School of Medicine)

Abstract

White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant’s brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41499-w
    DOI: 10.1038/s41467-023-41499-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41499-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-41499-w?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. Evelyn Tang & Chad Giusti & Graham L. Baum & Shi Gu & Eli Pollock & Ari E. Kahn & David R. Roalf & Tyler M. Moore & Kosha Ruparel & Ruben C. Gur & Raquel E. Gur & Theodore D. Satterthwaite & Danielle , 2017. "Developmental increases in white matter network controllability support a growing diversity of brain dynamics," Nature Communications, Nature, vol. 8(1), pages 1-16, December.
    2. Urs Braun & Anais Harneit & Giulio Pergola & Tommaso Menara & Axel Schäfer & Richard F. Betzel & Zhenxiang Zang & Janina I. Schweiger & Xiaolong Zhang & Kristina Schwarz & Junfang Chen & Giuseppe Blas, 2021. "Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Birk Diedenhofen & Jochen Musch, 2015. "cocor: A Comprehensive Solution for the Statistical Comparison of Correlations," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    4. Anthony Evans & Willem Sleegers & Žan Mlakar, 2020. "Individual differences in receptivity to scientific bullshit," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(3), pages 401-412, May.
    5. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    6. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    7. Walters, William H., 2017. "Do subjective journal ratings represent whole journals or typical articles? Unweighted or weighted citation impact?," Journal of Informetrics, Elsevier, vol. 11(3), pages 730-744.
    8. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    9. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    10. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    11. 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.
    12. Kathrin Leppek & Gun Woo Byeon & Wipapat Kladwang & Hannah K. Wayment-Steele & Craig H. Kerr & Adele F. Xu & Do Soon Kim & Ved V. Topkar & Christian Choe & Daphna Rothschild & Gerald C. Tiu & Roger We, 2022. "Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    13. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Kevin Handtke & Lisa Richter-Beuschel & Susanne Bögeholz, 2022. "Self-Efficacy Beliefs of Teaching ESD: A Theory-Driven Instrument and the Effectiveness of ESD in German Teacher Education," Sustainability, MDPI, vol. 14(11), pages 1-32, May.
    15. Yan Zhang & Antonios Garas & Frank Schweitzer, 2019. "Control Contribution Identifies Top Driver Nodes In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, December.
    16. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    17. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    18. repec:cup:judgdm:v:15:y:2020:i:2:p:193-202 is not listed on IDEAS
    19. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    20. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    21. Zabka, Philipp & Förster, Klaus-T. & Decker, Christian & Schmid, Stefan, 2024. "A centrality analysis of the Lightning Network," Telecommunications Policy, Elsevier, vol. 48(2).

    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:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41499-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.