Evidence map›Paper›PMID 37526293›Full record

ArticleeLife2023

Fine-grained functional parcellation maps of the infant cerebral cortex.

Fan Wang, Han Zhang, Zhengwang Wu, Dan Hu, Zhen Zhou, Jessica B Girault, Li Wang, Weili Lin, Gang Li

Abstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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  11. INDIVIDUALIZED TRAJECTORY PREDICTION OF EARLY DEVELOPING FUNCTIONAL CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025
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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

9 authors.

Fan WangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.ORCID 0000-0002-9955-020X
Han ZhangDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Zhengwang WuDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Dan HuDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Zhen ZhouDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Jessica B GiraultDepartment of Psychiatry, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Li WangDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Weili LinDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.
Gang LiDepartment of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel Hill, Chapel Hill, United States.ORCID 0000-0001-9585-1382

Funding

UNC/UMN Baby Connectome ProjectU01MH110274 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELISON, JED THOMAS, GILMORE, JOHN HORACE · 2016 to 2019
$4.5M
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
Genetic Liability for Autism and Infant Brain and Behavioral DevelopmentK01MH122779 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GIRAULT, JESSICA BULLINS · 2020 to 2024
$919k
Harmonizing and Archiving of Large-scale Infant Neuroimaging DataRF1MH123202 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2021
$627k
Infant Functional Connectome Fingerprinting based on Deep LearningR21MH127544 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2022
$272k
NIMH NIH HHS 1U01MH110274NIMH NIH HHS K01 MH122779NIMH NIH HHS MH116225NIMH NIH HHS MH117943NIMH NIH HHS MH123202NIMH NIH HHS MH127544NIMH NIH HHS R21 MH127544
6 · The paper itself

Abstract

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.

Indexed as

ConnectomeMagnetic Resonance ImagingBrainCerebral CortexHumansInfantNeuroimagingfunctional connectivityfunctional parcellationhumaninfant brainneuroscience

Identifiers

PMID37526293
PMCPMC10393291

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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.