Evidence map›Paper›PMID 39449114›Full record

SynthesisHuman brain mapping2024

Macroscale Gradient Dysfunction in Alzheimer's Disease: Patterns With Cognition Terms and Gene Expression Profiles.

Dawei Wang, Zhuangzhuang Li, Kun Zhao, Pindong Chen, Fan Yang, Hongxiang Yao, Bo Zhou, Yongbin Wei, Jie Lu, Yuqi Chen and 4 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Human brain mapping, 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. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

14 authors.

Dawei WangDepartment of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China.ORCID 0000-0002-4488-2135
Zhuangzhuang LiQueen Mary School Hainan, Beijing University of Posts and Telecommunications, Hainan, China.
Kun ZhaoQueen Mary School Hainan, Beijing University of Posts and Telecommunications, Hainan, China.
Pindong ChenSchool of Artificial Intelligence, University of Chinese Academy of Sciences, & Institute of Automation, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0003-4655-786X
Fan YangCAS Key Laboratory of Molecular Imaging, Institute of Automation, 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.
Yongbin WeiQueen Mary School Hainan, Beijing University of Posts and Telecommunications, Hainan, China.
Jie LuDepartment of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.ORCID 0000-0003-0425-3921
Yuqi ChenAffiliated Hospital, Beijing University of Posts and Telecommunications, Beijing, 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.
Pan WangDepartment of Neurology, Tianjin Huanhu Hospital, Tianjin, China.
Yong LiuQueen Mary School Hainan, Beijing University of Posts and Telecommunications, Hainan, China.ORCID 0000-0002-1862-3121

Funding

Beijing Natural Science Foundation 7244519China Postdoctoral Science Foundation 2021M691935National Natural Science Foundation of China 62333002National Natural Science Foundation of China 82172018Natural Science Foundation of Shandong Province ZR2021MH236Natural Science Foundation of Shandong Province ZR2023ZD14the Key R&D Program of Shandong Province 2022ZLGX03the Science and Technology Innovation 2030 Major Projects 2022ZD0211600
6 · The paper itself

Abstract

Macroscale functional gradient techniques provide a continuous coordinate system that extends from unimodal regions to transmodal higher-order networks. However, the alterations of these functional gradients in AD and their correlations with cognitive terms and gene expression profiles remain to be established. In the present study, we directly studied the functional gradients with functional MRI data from seven scanners. We adopted data-driven meta-analytic techniques to unveil AD-associated changes in the functional gradients. The principal primary-to-transmodal gradient was suppressed in AD. Compared to NCs, AD patients exhibited global connectome gradient alterations, including reduced gradient range and gradient variation, increased gradient scores in the somatomotor, ventral attention, and frontoparietal regions, and decreased in the default mode network. More importantly, the Gene Ontology terms of biological processes were significantly enriched in the potassium ion transport and protein-containing complex remodeling. Our compelling evidence provides a new perspective in understanding the connectome alterations in AD.

Indexed as

Alzheimer DiseaseConnectomeMagnetic Resonance ImagingTranscriptomeBrainDefault Mode NetworkHumansNerve NetAlzheimer's diseasefunctional gradientmicroscale transcriptome profile

Identifiers

PMID39449114
PMCPMC11502409

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.