Evidence map›Paper›PMID 42325271›Full record

ArticleiScience2026

Longitudinally altered default mode network and insula multimodal brain pattern in end-stage renal disease during sustained hemodialysis treatment.

Chuang Liang, Wenhao Jiang, Jiayu Chen, Jessica A Turner, Vince D Calhoun, Christopher C Abbott, Rongtao Jiang, Zening Fu, Lei Wu, Xiaoyan Wang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Chuang LiangDepartment of Artificial Intelligence and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.
Wenhao JiangDepartment of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China.
Jiayu ChenTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
Jessica A TurnerDepartment of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH 43210, USA.
Vince D CalhounTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
Christopher C AbbottDepartment of Psychiatry, University of New Mexico, Albuquerque, NM 87106, USA.
Rongtao JiangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Zening FuTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
Lei WuTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
Xiaoyan WangDepartment of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China.
Shile QiDepartment of Artificial Intelligence and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.
Yonggui YuanDepartment of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hemodialysis (HD) is the predominant treatment for end-stage renal disease (ESRD). Despite the efficacy of HD, the neurobiological underpinnings underlying high-risk complications remain unclear. In this study, using unsupervised fusion of functional and structural MRI, we identified a longitudinally altered default mode network (DMN)-insula pattern in ESRD receiving HD over 1-year follow-up (

Indexed as

health science

Identifiers

PMID42325271
PMCPMC13276593

What Socratic 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 Socratic graph.