Evidence map›Paper›PMID 36053245›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.

Fenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, Shunren Xia, Gang Li, UNC/UMN Baby Connectome Project Consortium

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. UNSUPERVISED CORTICAL SURFACE REGISTRATION NETWORK FOR ALIGNING GYRALNET.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025
    Article
  3. Review
  4. Article
  5. Fast Spherical Mapping of Cortical Surface Meshes Using Deep Unsupervised Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022
    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

7 authors.

Fenqiang ZhaoDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Zhengwang WuDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Li WangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Weili LinDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Shunren XiaKey Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China gang.
Gang LiDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
UNC/UMN Baby Connectome Project Consortium

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
Harmonizing and Archiving of Large-scale Infant Neuroimaging DataRF1MH123202 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2021
$627k
Volume-based Analysis of 6-month Infant Brain MRI for Autism Biomarker Identification and Early DiagnosisK01MH109773 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WANG, LI · 2017 to 2020
$571k
NIMH NIH HHS K01 MH109773NIMH NIH HHS R01 MH116225NIMH NIH HHS R01 MH117943NIMH NIH HHS RF1 MH123202NIMH NIH HHS U01 MH110274
6 · The paper itself

Abstract

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having

Indexed as

Infant cortical surface atlasSurface registration

Identifiers

PMID36053245
PMCPMC9432861

What OpenQuestion holds

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Registered trials

None linked

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.