Evidence map›Paper›PMID 31232554›Full record

ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2019

[A review on brain age prediction in brain ageing].

Lan Lin, Jingxuan Wang, Zhenrong Fu, Xuetao Wu, Shuicai Wu

Abstract readReview
In one paragraph

Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2019. 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. [A review on the application of UK Biobank in neuroimaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021
    Review
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.

Lan LinCollege of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.lanlin@bjut.edu.cn.
Jingxuan WangCollege of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.
Zhenrong FuCollege of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.
Xuetao WuCollege of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.
Shuicai WuCollege of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Indexed as

AgingNeuroimagingBrainHumansbrain agebrain ageingconvolution neural networkmachine learningneuroimageprediction model

Identifiers

PMID31232554
PMCPMC9929976

What OpenQuestion holds

Textmetadata
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.