Evidence map›Paper›PMID 37369585›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2023

Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains.

Ying Huang, Zhengwang Wu, Tengfei Li, Xifeng Wang, Ya Wang, Lei Xing, Hongtu Zhu, Weili Lin, Li Wang, Lei Guo and 2 more

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026
    Review
  3. Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  4. Revealing Fine-grained Genetically Informed Cortical Parcellation Maps of Neonates Based on Multi-view Spectral Clustering.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ying HuangDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.ORCID 0000-0003-2127-5751
Zhengwang WuDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Tengfei LiDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Xifeng WangDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516.
Ya WangDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Lei XingUNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599.ORCID 0000-0003-2325-0806
Hongtu ZhuDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516.
Weili LinDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Li WangDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Lei GuoSchool of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China.
John H GilmoreDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514.
Gang LiDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514 gang_li@med.unc.edu.

Funding

Preclinical CoreP50HD103573 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GABRIEL S DICHTER · 2020 to 2026
$9.7M
Early Brain Development in TwinsR01MH070890 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2004 to 2014
$5.3M
Early Brain Development in One and Two Year OldsR01HD053000 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2007 to 2018
$4.4M
Early Brain Development in TwinsU01MH070890 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2015 to 2019
$2.9M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Continued Development of Infant Brain Analysis ToolsR01MH117943 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG, WANG, LI · 2018 to 2022
$2.3M
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRIR01MH116225 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2018 to 2022
$1.9M
Harmonizing and Archiving of Large-scale Infant Neuroimaging DataRF1MH123202 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2021
$627k
NICHD NIH HHS P50 HD103573NICHD NIH HHS R01 HD053000NIMH NIH HHS R01 MH070890NIMH NIH HHS R01 MH116225NIMH NIH HHS R01 MH116527NIMH NIH HHS R01 MH117943NIMH NIH HHS RF1 MH123202NIMH NIH HHS U01 MH070890
6 · The paper itself

Abstract

Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.

Indexed as

BrainCerebral CortexAdultBrain MappingHumansInfantInfant, NewbornMagnetic Resonance ImagingNeuroimagingTwinscortical thicknessgenetic topographyneonatessurface area

Identifiers

PMID37369585
PMCPMC10451118

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.