Evidence map›Paper›PMID 42351064›Full record

ArticleBMC psychiatry2026

Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.

Peng Fang, Yifei Li, Shukun Zhu, Kangsheng Bi, Yanjun Li, Jiakuai Yu, Jiajia Zhang, Mingming Zheng, Jiajia Zhu, Daomin Zhu

Abstract read
In one paragraph

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

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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

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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

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5 · Who and what money

Authors and funding

10 authors.

Peng Fang *The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China.
Yifei Li *Department of Sleep Medicine, Affiliated Psychological Hospital of Anhui Medical University, Hefei, 230022, China.
Shukun ZhuThe School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China.
Kangsheng BiSchool of Mental Health, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Yanjun LiDepartment of Sleep Medicine, Affiliated Psychological Hospital of Anhui Medical University, Hefei, 230022, China.
Jiakuai YuDepartment of Sleep Medicine, Affiliated Psychological Hospital of Anhui Medical University, Hefei, 230022, China.
Jiajia ZhangDepartment of Sleep Medicine, Affiliated Psychological Hospital of Anhui Medical University, Hefei, 230022, China.
Mingming ZhengDepartment of Sleep Medicine, Affiliated Psychological Hospital of Anhui Medical University, Hefei, 230022, China.
Jiajia ZhuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Shushan District, Hefei, 230022, China. zhujiajiagraduate@163.com.
Daomin ZhuThe School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China. daominzhu1977@126.com.ORCID https://orcid.org/0009-0007-4843-7323

Funding

the Anhui Provincial Health and Wellness Scientific Research Project AHWJ2024Aa20549the National Clinical Key Specialty Construction Project of China AHWJ2023A20086
6 · The paper itself

Abstract

objectiveTo address the clinical difficulty of differentiating Generalized Anxiety Disorder (GAD) from Major Depressive Disorder (MDD), this study aimed to create a machine learning framework with strong interpretability, leveraging a comprehensive suite of resting-state functional MRI features to enhance potential relevance.

methodsA comprehensive set of functional measures, encompassing both local neural activity and global functional connectivity, was extracted as neuroimaging features from resting-state fMRI data. These data were obtained from 91 patients with Generalized Anxiety Disorder (GAD), 94 with Major Depressive Disorder (MDD), and 71 healthy controls (HCs). Five machine learning algorithms were used for classification, with performance estimated within a rigorous nested cross-validation framework. Then, partial correlations were conducted to assess the associations between top-ranked contributive neuroimaging features and symptom severity.

resultsFor the discrimination tasks, the machine learning models achieved area under the curve (AUC) values of 0.783 (GAD vs. MDD), 0.824 (GAD vs. HCs), and 0.867 (MDD vs. HCs), demonstrating moderate classification performance. Feature importance analysis revealed that precuneus function served as a prominently differential neurobiological signature and it showed opposing relationships with the severity of anxiety (positive correlation) and depression (negative correlation) across different diagnostic categories.

conclusionThese findings could guide the creation of a computational framework based on neuroimaging to effectively differentiate GAD from MDD. This is underscored by the pivotal role that the precuneus appears to play in the neurobiological processes associated with symptoms of both disorders.

trial registrationThis is a non-interventional study with a control group. Clinical trial registration is not applicable.

Indexed as

Anxiety DisordersGeneralized Anxiety DisorderMachine LearningMagnetic Resonance ImagingMajor Depressive DisorderAdultBrainCase-Control StudiesClassification AlgorithmsDiagnosis, DifferentialFemaleHumansMaleMiddle AgedDefault mode networkDifferential diagnosisGeneralized anxiety disorderMachine learningMajor depressive disorderNeuroimaging signaturesPrecuneusResting-State fMRI

Identifiers

PMID42351064
PMCPMC13579773

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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.