Evidence map›Paper›PMID 37954904›Full record

ArticleEClinicalMedicine2023

A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.

Kun Zhao, Pindong Chen, Aaron Alexander-Bloch, Yongbin Wei, Martin Dyrba, Fan Yang, Xiaopeng Kang, Dawei Wang, Dongsheng Fan, Shan Ye and 13 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

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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

23 authors at 14 institutions in 3 countries.

Kun ZhaoSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Pindong ChenSchool of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China.
Aaron Alexander-BlochDepartment of Psychiatry, University of Pennsylvania, Philadelphia, USA.
Yongbin WeiSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Martin DyrbaGerman Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany.
Fan YangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.
Xiaopeng KangSchool of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China.
Dawei WangDepartment of Radiology, Qilu Hospital of Shandong University, Ji'nan, China.
Dongsheng FanDepartment of Neurology, Peking University Third Hospital, Beijing, China.
Shan YeDepartment of Neurology, Peking University Third Hospital, Beijing, China.
Yi TangDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Hongxiang YaoDepartment of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Bo ZhouDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Jie LuDepartment of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Chunshui YuDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
Pan WangDepartment of Neurology, Tianjin Huanhu Hospital, Tianjin, China.
Zhengluan LiaoDepartment of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
Yan ChenDepartment of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
Longjian HuangAffiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
Xi ZhangDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Ying HanDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Shuyu LiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yong LiuSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Beijing University of Posts and Telecommunications · CNCapital Medical University · CNChinese PLA General Hospital · CNPeking University · CNUniversity of Chinese Academy of Sciences · CNZhejiang Provincial People's Hospital · CNAffiliated Hospital of Youjiang Medical University for Nationalities · CNBeijing Normal University · CNChildren's Hospital of Philadelphia · USGerman Center for Neurodegenerative Diseases · DEQilu Hospital of Shandong University · CNShandong Institute of Automation · CNTianjin Huanhu Hospital · CNTianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings: IBRAIN accurately differentiated individuals with AD from NCs ( Interpretation: Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials. Funding: Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.

Indexed as

Alzheimer's diseaseLongitudinal progressionMultisiteNeuroimaging biomarkerSpecific

Identifiers

PMID37954904
PMCPMC10632687
OpenAlexW4388208149

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.