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Premature birth changes wiring constraints in neonatal structural brain networks

Author

Listed:
  • Alexa Mousley

    (University of Cambridge)

  • Danyal Akarca

    (University of Cambridge
    Imperial College London
    Imperial College London)

  • Duncan E. Astle

    (University of Cambridge
    University of Cambridge)

Abstract

Structural brain organization in infancy is associated with later cognitive, behavioral, and educational outcomes. Due to practical limitations, such as technological advancements and data availability of fetal MRI, there is still much we do not know about the early emergence of topological organization. We combine the developing Human Connectome Project’s large infant dataset with generative network modeling to simulate the emergence of network organization over early development. Preterm infants had reduced connectivity, shorter connection lengths, and lower network efficiency compared to term-born infants. The models were able to recapitulate the organizational differences between term and preterm networks and revealed that preterm infant networks are better simulated under tighter wiring constraints than term infants. Tighter constraints for preterm models resulted in shorter connection lengths while preserving vital, long-range rich club connections. These simulations suggest that preterm birth is associated with a renegotiation of the cost-value wiring trade-off that may drive the emergence of different network organization.

Suggested Citation

  • Alexa Mousley & Danyal Akarca & Duncan E. Astle, 2025. "Premature birth changes wiring constraints in neonatal structural brain networks," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55178-x
    DOI: 10.1038/s41467-024-55178-x
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    References listed on IDEAS

    as
    1. Mikail Rubinov, 2016. "Constraints and spandrels of interareal connectomes," Nature Communications, Nature, vol. 7(1), pages 1-11, December.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    4. Danyal Akarca & Petra E. Vértes & Edward T. Bullmore & Duncan E. Astle, 2021. "A generative network model of neurodevelopmental diversity in structural brain organization," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
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