Evidence map›Paper›PMID 40606145›Full record

ArticleFrontiers in neurology2025

Brain age prediction model based on electroencephalogram signal and its application in children with autism spectrum disorders.

Yi Ju, Tong Zhao, Zaifen Gao, Wenguang Hu, Jiejian Luo, Nian Cheng, Chunli Liu, Yuwu Jiang, Bo Hong, Taoyun Ji and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Yi Ju *First Hospital, Peking University, Beijing, China.
Tong Zhao *Gnosis Healthineer Co., Ltd., Beijing, China.
Zaifen GaoJinan Children's Hospital, Jinan, China.
Wenguang HuChengdu Women and Children's Central Hospital, Chengdu, China.
Jiejian LuoGnosis Healthineer Co., Ltd., Beijing, China.
Nian ChengGnosis Healthineer Co., Ltd., Beijing, China.
Chunli LiuNingxia Hui Autonomous Region Maternal and Child Health Hospital, Yinchuan, China.
Yuwu JiangFirst Hospital, Peking University, Beijing, China.
Bo HongDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
Taoyun JiFirst Hospital, Peking University, Beijing, China.
Yuxiang YanGnosis Healthineer Co., Ltd., Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There is a lack of objective biomarkers for brain developmental abnormalities of autism spectrum disorder (ASD). We used EEG and deep learning to conduct a brain aging study in ASD. Methods: (1) A total of 659 healthy children and 98 ASD patients were retrospectively recruited. (2) An Auto-EEG-Brain AGE prediction model based on the Gate Recurrent Unit (GRU) neural network method was constructed. (3) Using the constructed model, we evaluated the difference between the brain age of ASD and that of healthy controls, and assessed the feasibility in the clinical assessment of ASD. Results: (1) The correlation coefficient ( Conclusion: We constructed the EEG-Brain AGE prediction model, which can identify an individual's brain development and be used as a biomarker for the brain development assessment in ASD.

Indexed as

autism spectrum disorderbrain AGE gap estimate (Brain AGE)EEGGate Recurrent Unit (GRU) neural network approachpediatric

Identifiers

PMID40606145
PMCPMC12213353

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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.