Evidence map›Paper›PMID 37014553›Full record

ArticleNeuroscience bulletin2023

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease.

Yida Qu, Pan Wang, Hongxiang Yao, Dawei Wang, Chengyuan Song, Hongwei Yang, Zengqiang Zhang, Pindong Chen, Xiaopeng Kang, Kai Du and 13 more

Open access · greenAbstract read
In one paragraph

Article in Neuroscience bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

23 authors at 8 institutions in 1 country.

Yida Qu *Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Pan Wang *Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China.
Hongxiang YaoDepartment of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China.
Dawei WangDepartment of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China.
Chengyuan SongDepartment of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China.
Hongwei YangDepartment of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
Zengqiang ZhangBranch of Chinese, PLA General Hospital, Sanya, 572022, China.
Pindong ChenBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Xiaopeng KangBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Kai DuBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Lingzhong FanBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Bo ZhouDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China.
Tong HanDepartment of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China.
Chunshui YuDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Xi ZhangDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China.
Nianming ZuoBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0002-6726-4575
Tianzi JiangBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0001-9531-291X
Yuying ZhouDepartment of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China.
Bing LiuBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Ying HanDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
Jie LuDepartment of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. imaginglu@hotmail.com.
Yong LiuBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. yongliu@bupt.edu.cn.ORCID http://orcid.org/0000-0002-1862-3121
Multi-Center Alzheimer’s Disease Imaging (MCADI) Consortium
Chinese Academy of Sciences · CNCapital Medical University · CNChinese PLA General Hospital · CNTianjin University · CNQilu Hospital of Shandong University · CNBeijing University of Posts and Telecommunications · CNTianjin Huanhu Hospital · CNTianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

Indexed as

Alzheimer DiseaseCognitive DysfunctionWhite MatterBrainCognitionDiffusion Tensor ImagingHumansReproducibility of ResultsAlzheimer’s diseaseCross-validationDiffusion tensor imagingWhite matter tracts

Identifiers

PMID37014553
PMCPMC10533766
OpenAlexW4362523476

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.