Evidence map›Paper›PMID 38839623›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2024

[

Le Xue, Yu Fu, Xin Gao, Gang Feng, Shufang Qian, Ling Wei, Lanlan Li, Cheng Zhuo, Hong Zhang, Mei Tian

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. The Hallmarks of Ageing in Microglia.Cellular and molecular neurobiology · 2025
    Review
  5. Review
  6. 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

10 authors.

Le Xue *Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yu Fu *College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China.
Xin Gao *Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China.
Gang FengShanghai Universal Medical Imaging Diagnostic Center, Shanghai, China.
Shufang QianDepartment of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Ling WeiHuashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
Lanlan LiHuashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
Cheng ZhuoCollege of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China.
Hong ZhangDepartment of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. hzhang21@zju.edu.cn.ORCID 0000-0002-4084-5150
Mei TianDepartment of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. tianmei@fudan.edu.cn.

Funding

Fundamental Research Funds for the Central Universities Fundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of China 2021YFE0108300National Natural Science Foundation of China 82030049National Natural Science Foundation of China 82361148130National Natural Science Foundation of China 82394430
6 · The paper itself

Abstract

purposeBrain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data.

methodsThe dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [

resultsBoth PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI).

conclusionThe integration of [

Indexed as

AgingBrainFluorodeoxyglucose F18Magnetic Resonance ImagingPositron-Emission TomographyAgedAged, 80 and overAlzheimer DiseaseCognitive DysfunctionDeep LearningFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedMultimodal ImagingFluorodeoxyglucose F18Brain ageBrain agingDeep learningMagnetic resonance imagingPositron emission tomography

Identifiers

PMID38839623

What OpenQuestion holds

Textmetadata
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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.