Evidence map›Paper›PMID 37065913›Full record

ArticleFrontiers in neuroscience2023

Correlation of abnormal brain changes with perinatal factors in very preterm infants based on diffusion tensor imaging.

Ying Liu, Binbin Nie, Yituo Wang, Fang He, Qiaozhi Ma, Tao Han, Guangjuan Mao, Jiqiang Liu, Haiping Zu, Xuetao Mu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. 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
0.9field-weighted citation impact, top 26% of its field
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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

11 authors at 4 institutions in 1 country.

Ying LiuSchool of Medical Imaging, Weifang Medical University, Weifang, Shandong, China.
Binbin NieBeijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Yituo WangDepartment of Radiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Fang HeChild Growth and Development Clinic, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Qiaozhi MaDepartment of Radiology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
Tao HanDepartment of Neonatology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Guangjuan MaoBeijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Jiqiang LiuDepartment of Magnetic Resonance Imaging, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Haiping ZuDepartment of Radiology, Specialized Medical Center of the Rocket Army, Beijing, China.
Xuetao MuDepartment of Radiology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
Bing WuDepartment of Radiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Chinese PLA General Hospital · CNChinese Academy of Sciences · CNWeifang Medical University · CNXinxiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: It remains unclear whether very preterm (VP) infants have the same level of brain structure and function as full-term (FT) infants. In addition, the relationship between potential differences in brain white matter microstructure and network connectivity and specific perinatal factors has not been well characterized. Objective: This study aimed to investigate the existence of potential differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA) and examine the potential association of these differences with perinatal factors. Methods: A total of 83 infants were prospectively selected for this study: 43 VP infants (gestational age, or GA: 27-32 weeks) and 40 FT infants (GA: 37-44 weeks). All infants at TEA underwent both conventional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Significant differences in white matter fractional anisotropy (FA) and mean diffusivity (MD) images between the VP and FT groups were observed using tract-based spatial statistics (TBSS). The fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then, a structural brain network was constructed, where the connection between each pair of nodes was defined by the number of fibers. Network-based statistics (NBS) were used to examine differences in brain network connectivity between the VP and FT groups. Additionally, multivariate linear regression was conducted to investigate potential correlations between fiber bundle numbers and network metrics (global efficiency, local efficiency, and small-worldness) and perinatal factors. Results: Significant differences in FA were observed between the VP and FT groups in several regions. These differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia (BPD), activity, pulse, grimace, appearance, respiratory (APGAR) score, gestational hypertension, and infection. Significant differences in network connectivity were observed between the VP and FT groups. Linear regression results showed significant correlations between maternal years of education, weight, the APGAR score, GA at birth, and network metrics in the VP group. Conclusions: The findings of this study shed light on the influence of perinatal factors on brain development in VP infants. These results may serve as a basis for clinical intervention and treatment to improve the outcome of preterm infants.

Indexed as

connectivity networksdiffusion tensor imagingperinatal factorspreterm infantstractography

Identifiers

PMID37065913
PMCPMC10101202
OpenAlexW4361269560

What OpenQuestion holds

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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 OpenQuestion graph.