Structural network analysis of brain development in young preterm neonates
Abstract
Preterm infants develop differently than those born at term and are at higher risk of brain pathology. Thus, an understanding of their development is of particular importance. Diffusion tensor imaging (DTI) of preterm infants offers a window into brain development at a very early age, an age at which that development is not yet fully understood. Recent works have used DTI to analyze structural connectome of the brain scans using network analysis. These studies have shown that, even from infancy, the brain exhibits small-world properties. Here we examine a cohort of 47 normal preterm neonates (i.e., without brain injury and with normal neurodevelopment at 18 months of age) scanned between 27 and 45 weeks post-menstrual age to further the understanding of how the structural connectome develops. We use full-brain tractography to find white matter tracts between the 90 cortical and sub-cortical regions defined in the University of North Carolina Chapel Hill neonatal atlas. We then analyze the resulting connectomes and explore the differences between weighting edges by tract count versus fractional anisotropy. We observe that the brain networks in preterm infants, much like infants born at term, show high efficiency and clustering measures across a range of network scales. Further, the development of many individual region-pair connections, particularly in the frontal and occipital lobes, is significantly correlated with age. Finally, we observe that the preterm infant connectome…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
During prenatal development, the whole brain network becomes increasingly efficient and segregated, with increased small-worldness and increased modularity.
“During prenatal development, the whole network becomes increasingly efficient and segregated with typical system-level refinements such as increased small-worldness (Brown et al., 2014) and increased modularity (Van Den Heuvel et al., 2015b).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Brown et al., 2014
Fetal brain structural connectivity shows adult-like topological organization, including significant small-world, modular, and rich-club organization, during the middle to final trimester of gestation.
“Many studies have discovered largely adult-like topological structures including significant small-world, modular, and rich-club organization during the middle to final trimester of gestation”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Brown et al., 2014
Cites
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256 · see citing works
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,653 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · see citing works
The Neonatal Connectome During Preterm Brain Development
van den Heuvel, M. P., Kersbergen, K. J., de Reus, M. A., Keunen, K., Kahn, R. S., Groenendaal, F., de Vries, L. S., & Benders, M. J. N. L. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu095 · Cited by 386 · see citing works
Rich-club organization of the newborn human brain
Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., Robinson, E. C., Ogundipe, E., Rueckert, D., Edwards, A. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324118111 · Cited by 360 · see citing works
Development Trends of White Matter Connectivity in the First Years of Life
Yap, P.-T., Fan, Y., Chen, Y., Gilmore, J. H., Lin, W., & Shen, D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024678 · Cited by 190 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., & Hamarneh, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.046 · Cited by 813 · see citing works
Self-citationZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“During prenatal development, the whole network becomes increasingly efficient and segregated with typical system-level refinements such as increased small-worldness [51] and increased modularity [463].”
BackgroundDubois, J., Adibpour, P., Poupon, C., Hertz-Pannier, L., & Dehaene-Lambertz, G. (2016) · Brain Plasticity · doi:10.3233/bpl-160031 · Cited by 40 · see citing works
- Authors
- Brown, C. J., Miller, S. P., Booth, B. G., Andrews, S., Chau, V., Poskitt, K. J., & Hamarneh, G.
- Affiliations
- Simon Fraser University (CA)
- University of Toronto (CA)
- BC Children's Hospital (CA)
- Corresponding author
- Brown, Colin J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 101
- Pages
- 667-680
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2024966367
- Cited by
- 103
- References
- 6
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 10 · 2023: 7 · 2022: 7 · 2021: 14 · 2020: 10 · 2019: 5 · 2018: 13 · 2017: 11 · 2016: 9 · 2015: 10
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NSERC (RGPIN298324)
- Imperial Order Daughters of the Empire and Children of the Junior Branch (IODE) Canada
- Government of Alberta
- Canadian Institutes of Health Research (CIHR) (MOP-79262)
- Tier 2 Canada Research Chair in Neonatal Neuroscience
- Michael Smith Foundation for Health Research
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- preterm infants, brain development, structural connectome, small-world networks, white matter tracts, fractional anisotropy, network efficiency, clustering coefficient, tractography, cortical connectivity, frontal lobe, occipital lobe, neonatal brain
- MeSH terms
- Brain, Female, Gestational Age, Humans, Infant, Newborn, Infant, Premature, Male, Nerve Net, Diffusion Tensor Imaging, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
