Evidence map›Paper›PMID 37842932›Full record

ArticleJournal of magnetic resonance imaging : JMRI2024

Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.

Camilo Calixto, Fedel Machado-Rivas, Davood Karimi, Clemente Velasco-Annis, Maria Camila Cortes-Albornoz, Onur Afacan, Simon K Warfield, Ali Gholipour, Camilo Jaimes

Abstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Advances in Fetal Brain Imaging.Magnetic resonance imaging clinics of North America · 2024
    Review
  6. White matter tract crossing and bottleneck regions in the fetal brain.bioRxiv : the preprint server for biology · 2024
    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

9 authors.

Camilo CalixtoComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Fedel Machado-RivasHarvard Medical School, Boston, Massachusetts, USA.
Davood KarimiComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Clemente Velasco-AnnisComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Maria Camila Cortes-AlbornozHarvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-0733-5105
Onur AfacanComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.ORCID 0000-0003-2112-3205
Simon K WarfieldComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Ali GholipourComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Camilo JaimesHarvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-7169-4244

Funding

Genetic Analysis and Manipulation Core (GAEC)P50HD105351 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI Hisashi Umemori · 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
Next-generation in-vivo fetal neuroimagingR01EB031849 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GHOLIPOUR-BABOLI, ALI · 2021 to 2024
$2.2M
Imaging early development of human neural circuitsR01HD109395 · NICHD · UNIVERSITY OF CALIFORNIA-IRVINE · PI ALI GHOLIPOUR-BABOLI · 2022 to 2026
$2.1M
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
Motion and Distortion Robust Diffusion Weighted Imaging Sequences for Pediatric PatientsR01NS121657 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI Onur Afacan · 2022 to 2026
$1.7M
Motion-robust super-resolution diffusion weighted MRI of early brain developmentR01EB018988 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI GHOLIPOUR-BABOLI, ALI · 2014 to 2017
$1.6M
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
American Roentgen Ray Society scholarshipBoston Children's HospitalNational Science Foundation (NSF) 212306NIBIB NIH HHS R01 EB018988NIBIB NIH HHS R01 EB019483NIBIB NIH HHS R01 EB031849NIBIB NIH HHS R01 EB032366NICHD NIH HHS P50 HD105351NICHD NIH HHS R01 HD109395NICHD NIH HHS R01 HD110772NIH HHS S10 OD025111NIH Office of the Director S10 OD25111NINDS NIH HHS R01 NS106030NINDS NIH HHS R01 NS121657NINDS NIH HHS R01 NS128281NLM NIH HHS R01 LM013608Rosamund Stone Zander Translational Neuroscience Centerthe Office of Faculty Development at Boston Children's Hospital
6 · The paper itself

Abstract

backgroundA lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. PURPOSE: To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). STUDY TYPE: Retrospective. POPULATION: Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. FIELD STRENGTH/SEQUENCE: 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). ASSESSMENT: We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. STATISTICAL TESTS: Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant.

resultsNetwork analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. DATA

conclusionExtensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Indexed as

BrainDiffusion Tensor ImagingEcho-Planar ImagingFetusGestational AgeAdolescentAdultAtlases as TopicFemaleHumansImage Processing, Computer-AssistedMiddle AgedPregnancyRetrospective StudiesYoung Adultcomplex network analysisfetal brainin utero imagingstructural connectome

Identifiers

PMID37842932
PMCPMC11018715

What OpenQuestion holds

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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 OpenQuestion graph.