Evidence map›Paper›PMID 38026184›Full record

ArticleiScience2023

A common spectrum underlying brain disorders across lifespan revealed by deep learning on brain networks.

Mianxin Liu, Jingyang Zhang, Yao Wang, Yan Zhou, Fang Xie, Qihao Guo, Feng Shi, Han Zhang, Qian Wang, Dinggang Shen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2023. 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
0.9field-weighted citation impact, top 27% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  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

10 authors at 4 institutions in 1 country.

Mianxin LiuSchool of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
Jingyang ZhangSchool of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
Yao WangDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, China.
Yan ZhouDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, China.
Fang XiePET Center, Huashan Hospital, Fudan University, Shanghai 200040, China.
Qihao GuoDepartment of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.
Feng ShiDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai 200232, China.
Han ZhangSchool of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
Qian WangSchool of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
Dinggang ShenSchool of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
ShanghaiTech University · CNShanghai Jiao Tong University · CNFudan University · CNUnited Imaging Healthcare (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key neuroimaging evidence remains unrevealed for elucidating such commonness and the relationships among these disorders. To explore this puzzle, we build a restricted single-branch deep learning model, using multi-site functional magnetic resonance imaging data (

Indexed as

BioinformaticsBiological sciencesHealth sciencesHealth technologyMedical informaticsMedicine

Identifiers

PMID38026184
PMCPMC10651682
OpenAlexW4387740460

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.