Evidence map›Paper›PMID 41029207›Full record

ArticleBMC psychiatry2025

High-resolution structural magnetic resonance examination of the Habenula in patients with first-episode depression: an exploratory radiomics diagnostic value analysis based on cluster analysis.

Lin Hou, BingYang Bian, ShuXin Luan, XingChen Pan, MingYang Li, Han Xue, HongChao Wang, HuiMao Zhang, Lei Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

9 authors.

Lin HouDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
BingYang BianDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
ShuXin LuanDepartment of Mental Health, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
XingChen PanDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
MingYang LiDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
Han XueDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
HongChao WangDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
HuiMao ZhangDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
Lei ZhangDepartment of Radiology, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, 130021, People's Republic of China. zlei99@jlu.edu.cn.

Funding

Jilin Provincial Key Laboratory of Medical Imaging & Big Data No. 20200601003JCthe National Natural Science Foundation of China No. 82102143Youth Development Foundation of the First Hospital of Jilin University No. JDYY15202413
6 · The paper itself

Abstract

backgroundThe habenula (Hb) is a vital hub for the monoaminergic pathway and plays a crucial role in depression pathophysiology. However, owing to its small size and heterogeneity between individuals, there is no consensus on imaging alterations in the Hb in depression. This study aimed to examine the differences in the Hb between healthy controls (HCs) and patients with first-episode depression (FED) who were not taking any antidepressants, and to assess the value of Hb voxel cluster radiomic features in discriminating patients with FED from HCs.

methodsThis cross-sectional study included 94 participants (47 HCs and 47 patients with FED) who underwent 3-T magnetic resonance imaging. Differences in the Hb volume and T1 values between the two groups were examined. Correlations among volume, T1 value, depression severity, and age were also examined. Furthermore, a clustering-based radiomics model to differentiate patients with FED from HCs was developed and validated.

resultsIn HCs, the Hb T1 value was positively related to age, whereas that of patients with FED showed no significant correlation. The prediction performance was improved in the clustering-based radiomics model (area under the curve [AUC] = 0.844) compared with the traditional model (AUC = 0.708).

conclusionsOur findings imply that the Hb and its internal heterogeneity are imaging markers for depression studies.

trial registrationNot applicable.

Indexed as

HabenulaMagnetic Resonance ImagingAdultCluster AnalysisCross-Sectional StudiesFemaleHumansMaleMiddle AgedRadiomicsYoung AdultClassificationHabenulaMagnetic resonance imagingMajor depressive disorderRadiomics

Identifiers

PMID41029207
PMCPMC12487362

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.