Evidence map›Paper›PMID 37664406›Full record

ArticleArXiv2023

Characterizing normal perinatal development of the human brain structural connectivity.

Yihan Wu, Lana Vasung, Camilo Calixto, Ali Gholipour, Davood Karimi

Abstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

5 authors.

Yihan WuComputational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
Lana VasungDepartment of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Camilo CalixtoComputational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
Ali GholipourComputational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
Davood KarimiComputational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.

Funding

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
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
NIBIB NIH HHS R01 EB031849NIBIB NIH HHS R01 EB032366NICHD NIH HHS R01 HD109395NICHD NIH HHS R01 HD110772NINDS NIH HHS R01 NS106030NINDS NIH HHS R01 NS128281
6 · The paper itself

Abstract

Early brain development is characterized by the formation of a highly organized structural connectome. The interconnected nature of this connectome underlies the brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development and imaging difficulties. Combined with high inter-subject variability, these factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational framework, based on spatio-temporal averaging, for determining such baselines. We used this framework to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in perinatal stage. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. We observed increases in global and local efficiency, a decrease in characteristic path length, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.

Indexed as

brain atlasesdiffusion MRIneonatal brainstructural brain connectivity

Identifiers

PMID37664406
PMCPMC10473780

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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.