Evidence map›Paper›PMID 36684838›Full record

ArticleFrontiers in human neuroscience2022

Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder.

Fenfen Sun, Yue Chen, Yingwen Huang, Jing Yan, Yihong Chen

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Fenfen SunCenter for Brain, Mind, and Education, Shaoxing University, Shaoxing, China.
Yue ChenCenter for Brain, Mind, and Education, Shaoxing University, Shaoxing, China.
Yingwen HuangCenter for Brain, Mind, and Education, Shaoxing University, Shaoxing, China.
Jing YanCenter for Brain, Mind, and Education, Shaoxing University, Shaoxing, China.
Yihong ChenDepartment of Otorhinolaryngology, The First People's Hospital of Xiaoshan, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The present study used magnetic resonance imaging to investigate the difference in the relationship between gray matter structure and age in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD) subjects. Methods: After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents (7-18 years old) with high-functioning ASD and 151 TD subjects matched for age, sex and full IQ were included in the current study. By using the voxel-based morphological analysis method, gray matter volume/density (GMV/GMD) maps were obtained for each participant. Then, a multiple regression analysis was performed for ASD and TD groups, respectively to estimate the relationship between GMV/GMD and age with gender, education, site, and IQ scores as covariates. Furthermore, a Results: Results showed that compared with TD, the GMD of ASD showed stronger positive correlations with age in the prefrontal cortex, and a stronger negative correlation in the left inferior parietal lobule, and a weaker positive correlation in the right inferior parietal lobule. The GMV of ASD displayed stronger positive correlations with age in the prefrontal cortex and cerebellum. Conclusion: These findings may provide evidence to support that the brain structure abnormalities underlying ASD during childhood and adolescence may differ from each other.

Indexed as

ageautism spectrum disorder (ASD)children and adolescentsgray matter structureregression analysis

Identifiers

PMID36684838
PMCPMC9853167

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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.