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Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults
Open accessAbstract
Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70 × 70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.
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“After birth until adulthood, the brain network reshapes to improve integration capacity with the reconfigurations of decreased modularity and decreased small-worldness”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Dennis et al., 2013
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Metadata
- Authors
- Dennis, E. L., Jahanshad, N., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I. B., Toga, A. W., Wright, M. J., & Thompson, P. M.
- Affiliations
- University of California, Los Angeles (US)
- The University of Queensland (AU)
- QIMR Berghofer Medical Research Institute (AU)
- The University of Sydney (AU)
- Corresponding author
- Thompson, Paul M.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 64
- Pages
- 671-684
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3603574
- OpenAlex
- W1859137208
- Cited by
- 222
- References
- 75
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 15 · 2023: 8 · 2022: 13 · 2021: 9 · 2020: 23 · 2019: 13 · 2018: 14 · 2017: 25 · 2016: 22 · 2015: 18 · 2014: 23 · 2013: 16
- FWCI
- 14.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Faculty of Health; Institute of Health and Biomedical Innovation
- Funders
- National Health and Medical Research Council (1009064, 389875, 486682)
- U.S. National Library of Medicine (T15 LM07356)
- National Institute of Child Health and Human Development (R01 HD050735)
- National Institutes of Health (T32MH073526-06, P41 RR013642, EB008432, EB008281, EB007813)
- 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
- structural brain connectivity, brain maturation, structural connectome, network metrics, fiber density, cortical networks, frontal cortex, temporal cortex, adolescents, young adults, tractography
- MeSH terms
- Adolescent, Adult, Aging, Algorithms, Cerebral Cortex, Child, Female, Humans, Image Interpretation, Computer-Assisted, Male, Nerve Fibers, Myelinated, Sensitivity and Specificity, Reproducibility of Results, Young Adult, 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 ›
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