Evidence map›Paper›PMID 42463653›Full record

ArticleNature communications2026

Biophysical modeling of anatomically realistic prenatal cortical folding development.

Jixin Hou, Zhengwang Wu, Kun Jiang, Taotao Wu, Lu Zhang, Dajiang Zhu, Wei Gao, Mir Jalil Razavi, Tianming Liu, Ellen Kuhl and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Jixin HouSchool of ECAM, College of Engineering, University of Georgia, Athens, GA, USA.
Zhengwang WuDepartment of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0003-4436-9005
Kun JiangSchool of ECAM, College of Engineering, University of Georgia, Athens, GA, USA.ORCID http://orcid.org/0009-0002-5569-5498
Taotao WuSchool of Chemical, Materials, and Biomedical Engineering, College of Engineering, University of Georgia, Athens, GA, USA.
Lu ZhangDepartment of Computer Science, Indiana University-Indianapolis, Indianapolis, IN, USA.
Dajiang ZhuDepartment of Computer Science, University of Texas at Arlington, Arlington, TX, USA.
Wei GaoDepartment of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9260-2601
Mir Jalil RazaviDepartment of Mechanical Engineering, Binghamton University, Binghamton, NY, USA.ORCID http://orcid.org/0000-0002-8421-9688
Tianming LiuSchool of Computing, University of Georgia, Athens, GA, USA.
Ellen KuhlDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6283-935X
Gang LiDepartment of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0001-9585-1382
Xianqiao WangSchool of ECAM, College of Engineering, University of Georgia, Athens, GA, USA. xqwang@uga.edu.ORCID http://orcid.org/0000-0003-2461-3015

Funding

Continued Development of Infant Neuroimaging Analysis ToolsR01EB037388 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Gang Li, Li Wang · 2025 to 2026
$1.2M
SCH: Using Data-Driven Computational Biomechanics to Disentangle Brain Structural Commonality, Variability, and Abnormality in ASDR01NS135574 · NINDS · UNIVERSITY OF GEORGIA · PI Xianqiao Wang · 2023 to 2026
$1.1M
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) EB037388National Science Foundation (NSF) IIS-2011369NIBIB NIH HHS R01 EB037388NINDS NIH HHS R01 NS135574U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1R01NS135574-01
6 · The paper itself

Abstract

Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically realistic cortical geometry to enable biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating heterogeneous growth yields folding patterns that match key anatomical landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development.

Indexed as

Cerebral CortexFemaleFetusHumansLissencephalyMagnetic Resonance ImagingMorphogenesisNeurodevelopmentPolymicrogyriaPregnancy

Identifiers

PMID42463653
PMCPMC13490475

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.