Evidence map›Paper›PMID 40177647›Full record

ArticleResearch (Washington, D.C.)2025

Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation.

Minxin Guo, Heng Zhang, Yuanyuan Huang, Yunheng Diao, Wei Wang, Zhaobo Li, Shixuan Feng, Jing Zhou, Yuping Ning, Fengchun Wu and 1 more

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Neural Circuit Taxonomy and Precision Psychiatry in Major Depression.Advances in experimental medicine and biology · 2026
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. 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

11 authors.

Minxin GuoSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Heng ZhangSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Yuanyuan HuangDepartment of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Yunheng DiaoSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Wei WangSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Zhaobo LiSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Shixuan FengDepartment of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Jing ZhouSchool of Material Science and Engineering, South China University of Technology, Guangzhou, China.
Yuping NingDepartment of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Fengchun WuDepartment of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Kai WuSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.ORCID https://orcid.org/0000-0003-4467-1597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies have indicated that major depressive disorder (MDD) patients with suicidal ideation (SI) present abnormal functional connectivity (FC) and network organization in node-centric brain networks, ignoring the interactions among FCs. Whether the abnormalities of edge interactions affect the emergence of SI and are related to the gene expression remains largely unknown. In this study, resting-state functional magnetic resonance imaging (fMRI) data were collected from 90 first-episode, drug-naive MDD with suicidal ideation (MDDSI) patients, 60 first-episode, drug-naive MDD without suicidal ideation (MDDNSI) patients, and 98 healthy controls (HCs). We applied the methodology of edge-centric network analysis to construct the functional brain networks and calculate the nodal entropy. Furthermore, we examined the relationships between nodal entropy alterations and gene expression. The MDDSI group exhibited significantly lower subnetwork entropy in the dorsal attention network (DAN) and significantly greater subnetwork entropy in the default mode network than the MDDNSI group. The visual learning score of the measurement and treatment research to improve cognition in schizophrenia (MATRICS) consensus cognitive battery was negatively correlated with the subnetwork entropy of DAN in the MDDSI group. The support vector machine model based on nodal entropy achieved an accuracy of 81.87% when distinguishing the MDDNSI and MDDSI. Additionally, the changes in SI-related nodal entropy were associated with the expression of genes in cell signaling and interactions, as well as immune and inflammatory responses. These findings reveal the abnormalities in nodal entropy between the MDDSI and MDDNSI groups, demonstrated their association with molecular functions, and provided novel insights into the neurobiological underpinnings and potential markers for the prediction and prevention of suicide.

Identifiers

PMID40177647
PMCPMC11964328

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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.