Evidence map›Paper›PMID 32004807›Full record

ArticleBrain and language2020

Altered efficiency of white matter connections for language function in children with language disorder.

Min-Hee Lee, Nolan B O'Hara, Michael E Behen, Jeong-Won Jeong

Abstract read
In one paragraph

Article in Brain and language, 2020. 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
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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

4 authors.

Min-Hee LeeTranslational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Carman and Ann Adams Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA.
Nolan B O'HaraTranslational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA.
Michael E BehenTranslational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Carman and Ann Adams Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA; Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA.
Jeong-Won JeongTranslational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Carman and Ann Adams Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA; Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA. Electronic address: jjeong@med.wayne.edu.

Funding

Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery.R01NS089659 · NINDS · WAYNE STATE UNIVERSITY · PI JEONG, JEONG-WON · 2015 to 2025
$3.7M
NINDS NIH HHS R01 NS089659
6 · The paper itself

Abstract

To characterize structural white matter substrates associated with language functions in children with language disorders (LD), a psychometry-driven diffusion tractography network was investigated with canonical correlation analysis (CCA), which can reliably predict expressive and receptive language scores from the nodal efficiency (NE) of the obtained network. The CCA found that the NE values of six regions: left inferior-frontal-opercular, left insular, left angular gyrus, left superior-temporal-gyrus, right hippocampus, and right cerebellar-lobule were highly correlated with language scores (ρ

Indexed as

CerebellumChildChild, PreschoolDiffusion Tensor ImagingFemaleFrontal LobeHippocampusHumansLanguage DisordersMaleWhite MatterCanonical correlation analysisDWI networkLanguage disorderNodal efficiency

Identifiers

PMID32004807
PMCPMC9022213

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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.