Evidence map›Paper›PMID 39333619›Full record

ArticleScientific reports2024

Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.

Shi-Yi Qi, Si-Jia Zhang, Li-Li Lin, Yu-Rong Li, Jian-Guo Chen, You-Cong Ni, Xin Du, Jie Zhang, Pin Ge, Gui-Hua Liu and 7 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

17 authors.

Shi-Yi Qi *Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Si-Jia Zhang *Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Li-Li Lin *Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Yu-Rong LiDepartment of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian Province, China.
Jian-Guo ChenDepartment of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian Province, China.
You-Cong NiSchool of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian Province, China.
Xin DuSchool of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian Province, China.
Jie ZhangDepartment of Rehabilitation, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Pin GeFujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China.
Gui-Hua LiuFujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China.
Jiang-Yun WuDepartment of Rehabilitation, The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Shen LinFujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China.
Meng GongDepartment of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Jin-Wen LinDepartment of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Lan-Fang ChenDepartment of Rehabilitation, The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Ling-Ling HeDepartment of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
Dong LinDepartment of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China. bluelike1@sina.com.

Funding

the National Natural Science Foundation of China grant agreement no.82074521
6 · The paper itself

Abstract

In a multicenter case-control investigation, we assessed the efficacy of the Electrooculogram Signal Analysis (EOG-SA) method, which integrates attention-related visual evocation, electrooculography, and nonlinear analysis, for distinguishing between intellectual and developmental disabilities (IDD) and typical development (TD) in children. Analyzing 127 participants (63 IDD, 64 TD), we applied nonlinear dynamics for feature extraction. Results indicated EOG-SA's capability to distinguish IDD, with higher template thresholds and Correlation Dimension values correlating with clinical severity. The template threshold proved a robust indicator, with higher values denoting severe IDD. Discriminative metrics showed areas under the curve of 0.91 (template threshold) and 0.85/0.91 (D2), with sensitivities and specificities of 77.6%/95.9% and 93.5%/71.0%, respectively. EOG-SA emerges as a promising tool, offering interpretable neural biomarkers for early and nuanced diagnosis of IDD.

Indexed as

AttentionDevelopmental DisabilitiesElectrooculographyIntellectual DisabilityAdolescentCase-Control StudiesChildChild, PreschoolFemaleHumansMaleSignal Processing, Computer-AssistedAttentionElectrooculogram (EOG) signalsEye movementIntellectual and developmental disabilitiesNeurodevelopmental disordersNonlinear analysis techniques

Identifiers

PMID39333619
PMCPMC11437286

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