Evidence map›Paper›PMID 35962721›Full record

SynthesisAutism research : official journal of the International Society for Autism Research2022

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies.

Min Li, Yide Wang, Masaya Tachibana, Shafiur Rahman, Kuriko Kagitani-Shimono

Open access · hybridAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in Autism research : official journal of the International Society for Autism Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 2 pooled it
6.0field-weighted citation impact, top 3% 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

30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.

  1. Pooled it
  2. Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies.Autism research : official journal of the International Society for Autism Research · 2022
    Pooled it
  3. Widespread alterations in white matter microstructure in autism: A multilevel meta-analysis.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026
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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

5 authors at 2 institutions in 1 country.

Min LiDepartment of Child Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan.ORCID 0000-0002-9202-1399
Yide WangDepartment of Child Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan.
Masaya TachibanaDepartment of Child Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan.ORCID 0000-0002-6761-7158
Shafiur RahmanDepartment of Child Development, United Graduate School of Child Development, Hamamatsu University School of Medicine, Higashi-ku, Hamamatsu, Shizuoka, Japan.ORCID 0000-0003-1911-6807
Kuriko Kagitani-ShimonoDepartment of Child Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan.ORCID 0000-0001-9649-9499
The University of Osaka · JPHamamatsu University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta-analysis aimed to comprehensively elucidate the abnormality in language-related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta-regression analysis. Thirty-three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta-analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language-associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under-connectivity hypothesis and demonstrate the widespread abnormal microstructure of language-related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere. LAY SUMMARY: This meta-analysis explored abnormalities in white matter connectivity in language networks of individuals with ASD. Significantly reduced white matter integrity was found in all language-associated tracts in subjects with ASD compared with TD controls. In addition, structural disturbances of language networks in the autistic brain exhibit a leftward tendency, and more prominent abnormalities are observed in younger people with ASD than in adults.

Indexed as

Autism Spectrum DisorderChild Development Disorders, PervasiveWhite MatterAdultBrainChildDiffusion Tensor ImagingHumansautism spectrum disorderdiffusion tensor imaginglanguage networksmeta-analysiswhite matter connectivity

Identifiers

PMID35962721
PMCPMC9546367
OpenAlexW4291197576

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.