Evidence map›Paper›PMID 42017053›Full record

ArticlePsychoradiology2026

Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging.

Ruoxi Lu, Jianyu Li, Yiran Li, Xinglin Zeng, Yan Guo, Danian Li, Ying Cui, Xinyu Liang, Hanyue Zhang, Jing Wang and 4 more

Erratum issuedAbstract read
In one paragraph

Article in Psychoradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Ruoxi LuT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Jianyu LiT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Yiran LiDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Xinglin ZengDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Yan GuoT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Danian LiCerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Ying CuiCerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China.
Xinyu LiangT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Hanyue ZhangDepartment of Radiology, Foshan first People's Hospital, Foshan 528000, China.
Jing WangT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Baohua ChengT he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Yujie LiuDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510400, China.
Ze WangDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.ORCID https://orcid.org/0000-0002-8339-5567
Shijun QiuDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510400, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Major depressive disorder (MDD) in adolescents and young adults is increasingly prevalent, yet accurate diagnosis remains challenging due to the limitations of conventional neuroimaging metrics. Traditional resting-state functional magnetic resonance imaging (rs-fMRI) measures such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity density (FCD) primarily capture static aspects of brain activity and may overlook critical neural dynamics. Brain entropy (BEN), which quantifies temporal irregularity in rs-fMRI signals, may offer a complementary approach to better characterize neural alterations in MDD. Methods: We analyzed multimodal rs-fMRI data from 204 individuals aged 12-24 years (119 with MDD and 85 healthy controls). BEN was computed alongside ALFF, ReHo, and FCD to extract region-wise features across the brain. A support vector machine with recursive feature elimination (SVM-RFE) was used to classify MDD and healthy controls based on various feature combinations. Classification performance was evaluated using repeated cross-validation and permutation testing. Additionally, partial Spearman correlations were performed between selected brain features and clinical measures including depression severity, childhood trauma, sleep quality, and cognitive control. Results: Models incorporating BEN consistently outperformed those using traditional rs-fMRI features alone. The combination of BEN, ALFF, and FCD achieved the highest classification accuracy (AUC = 0.877, permutation test Conclusions: This study demonstrates that BEN provides complementary diagnostic information to traditional rs-fMRI features in classifying adolescent and young adult MDD. BEN-related alterations in brain activity may reflect underlying neurobiological disruptions and show potential as a functional neuroimaging biomarker for depression during a critical stage of brain development.

Indexed as

adolescentbrain entropymachine learningmajor depressive disorderresting-state fMRIyoung adult

Identifiers

PMID42017053
PMCPMC13092983

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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.