Evidence map›Paper›PMID 41000794›Full record

ArticlebioRxiv : the preprint server for biology2025

Charting the Normal Development of Structural Brain Connectivity in Utero using Diffusion MRI.

Davood Karimi, Bo Li, Athena Taymourtash, Camilo Jaimes, Ellen P Grant, Simon K Warfield

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Davood KarimiDepartment of Radiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, USA.
Bo LiDepartment of Radiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, USA.
Athena TaymourtashElectrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Camilo JaimesDepartment of Radiology, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts, USA.
Ellen P GrantDepartment of Radiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, USA.ORCID 0000-0003-1005-4013
Simon K WarfieldDepartment of Radiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, USA.

Funding

Enabling the Assessment of Fetal Brain Development and Degeneration with Machine LearningR01NS128281 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI Davood Karimi · 2023 to 2026
$1.8M
Accurate, reliable, and interpretable machine learning for assessment of neonatal and pediatric brain micro-structureR01HD110772 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI Davood Karimi · 2023 to 2026
$1.5M
Machine learning algorithms to analyze large medical image datasetsR01LM013608 · NLM · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2021 to 2024
$1.5M
Bringing Coherent Fetal Brain Volumes and Automated Metrics to the Radiology WorkflowR01EB036945 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI ELFAR ADALSTEINSSON, Patricia Ellen Grant · 2025 to 2026
$1.3M
NIBIB NIH HHS R01 EB036945NICHD NIH HHS R01 HD110772NINDS NIH HHS R01 NS128281NLM NIH HHS R01 LM013608
6 · The paper itself

Abstract

Understanding the structural connectivity of the human brain during fetal life is essential for uncovering the early foundations of neural function and vulnerability to developmental disorders. Diffusion-weighted MRI (dMRI) enables noninvasive mapping of white-matter pathways and construction of the brain's structural connectome, but its application to the fetal brain has been constrained by limited data and the technical challenges of fetal dMRI analysis. Here, we present the largest study to date of in utero brain connectivity, analyzing high-quality dMRI data from 198 fetuses between 22 and 37 gestational weeks from the Developing Human Connectome Project. We applied advanced fetal-specific tools for brain segmentation, parcellation, and tractography, and used fiber bundle capacity to weight connections. We reconstructed individual structural connectomes and characterized their developmental trajectories. Graph-theoretical analysis revealed consistent increases in both integration and segregation metrics across gestation, alongside stable small-world properties. Bootstrapping confirmed the robustness of nodal and edge-wise developmental patterns, and a sigmoid growth model identified a narrow time window (approximately 27.5-30.5 weeks) of rapid connectivity strengthening. In addition, we introduced a new method for constructing age-specific connectome templates by aggregating individual subject connectomes. Our analysis shows that this approach outperforms spatial alignment and image-space averaging, yielding templates that preserve individual topology and support accurate age prediction. Together, these findings provide a reasonable normative map of fetal brain structural connectivity and establish a foundation for future studies of atypical development and early indicators of neurological risk.

Identifiers

PMID41000794
PMCPMC12458105

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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.