Evidence map›Paper›PMID 39812160›Full record

ArticleHuman brain mapping2025

White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain.

Camilo Calixto, Matheus D Soldatelli, Bo Li, Lana Vasung, Camilo Jaimes, Ali Gholipour, Simon K Warfield, Davood Karimi

Abstract read
In one paragraph

Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Camilo CalixtoComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-5500-9721
Matheus D SoldatelliComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Bo LiComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Lana VasungDepartment of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Camilo JaimesMassachusetts General Hospital, Boston, Massachusetts, USA.
Ali GholipourComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Simon K WarfieldComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Davood KarimiComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.

Funding

Genetic Analysis and Manipulation Core (GAEC)P50HD105351 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI SCOTT Loren POMEROY, MUSTAFA SAHIN · 2021 to 2026
$9.4M
Improved Motion Robust MRI of ChildrenR01EB019483 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2015 to 2024
$3.9M
Motion Compensated fMRI for Pre-Surgical Planning in EpilepsyR01NS124212 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI SIMON K WARFIELD · 2023 to 2026
$2.6M
Next-generation in-vivo fetal neuroimagingR01EB031849 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GHOLIPOUR-BABOLI, ALI · 2021 to 2024
$2.2M
Enhanced Imaging of the Fetal Brain MicrostructureR01EB032366 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GHOLIPOUR-BABOLI, ALI · 2022 to 2025
$2.0M
Acquisition of a Siemens 3T MRI for Research ImagingS10OD025111 · OD · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2018 to 2018
$2.0M
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
Advancing Microstructural and Vascular Neuroimaging in Perinatal StrokeR01NS106030 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2019 to 2023
$1.7M
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
National Institutes of Health (NIH) R01EB019483National Institutes of Health (NIH) R01EB031849National Institutes of Health (NIH) R01EB032366National Institutes of Health (NIH) R01HD110772National Institutes of Health (NIH) R01LM013608National Institutes of Health (NIH) R01NS106030National Institutes of Health (NIH) R01NS124212National Institutes of Health (NIH) R01NS128281National Institutes of Health (NIH) S10OD025111NIBIB NIH HHS R01 EB019483NIBIB NIH HHS R01 EB031849NIBIB NIH HHS R01 EB032366NICHD NIH HHS P50 HD105351NICHD NIH HHS R01 HD110772NIH HHS S10 OD025111NINDS NIH HHS R01 NS106030NINDS NIH HHS R01 NS124212NINDS NIH HHS R01 NS128281NLM NIH HHS R01 LM013608
6 · The paper itself

Abstract

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

Indexed as

BrainFetusNeural PathwaysWhite MatterDiffusion Tensor ImagingFemaleGestational AgeHumansImage Processing, Computer-AssistedMalePregnancydiffusion MRIfetal brainfiber bottlenecksfiber crossingsstructural connectivitywhite matter tracts

Identifiers

PMID39812160
PMCPMC11733681

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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.