REST-meta-MDD Project
English
United States, Provo
Maps Project INDI Data

数据描述

REST-meta-MDD Project

The REST-meta-MDD dataset from the DIRECT consortium is a multi-site resting-state fMRI derivative resource for studying major depressive disorder, aggregating 2,428 previously collected participants from 25 cohorts across 17 hospitals in China (1,300 MDD patients and 1,128 matched normal controls). It provides final preprocessed R-fMRI indices computed locally using a standardized DPARSF-based pipeline with motion confound control (including Friston-24 regression and framewise displacement correction) to reduce analytic heterogeneity. Associated phenotypic information includes sex, first-episode versus recurrent status, and antidepressant medication status where available. The dataset supports replication, secondary analyses, and biomarker discovery while protecting participant privacy.

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相关论文

10

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

Gallo SeleneEl-Gazzar AhmedZhutovsky PaulThomas Rajat M.Javaheripour Nooshin30
Molecular Psychiatry
2023
2023/2/15
00 p.1-10
The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we...
DepressionDiagnostic markers
10.1038/S41380-023-01977-5
ISSN:1359-4184

Stable depression subtypes identified using functional connectome normative deviation models and their response to rTMS

Chengfeng ChenLiyuan LinYuan LiuShiying WangJiang Wang9
Molecular Psychiatry
2026
2026/5/7
00 p.1-10
The heterogeneity of depression complicates treatment. Identifying stable biological subtypes could advance precision-targeted interventions. This study aims to identify stable depression subtypes using functional connectome normative deviation models and to assess their response to repetitive trans...
DepressionNeurosciencePrognostic markers
10.1038/S41380-026-03634-Z
ISSN:1359-4184

Enhancing depression diagnosis with augmented brain signal driven decorrelated graph neural networks

Jyotismita BarmanMohammad YusufSandeep KumarTapan Kumar Gandhi
Communications Medicine
2026
2026/3/2
Vol.6 No.1 p.2110
Major Depressive Disorder (MDD) is a leading global neuropsychiatric disorder, requiring precise diagnosis for effective intervention. Developing accurate diagnostic models for MDD remains a critical but challenging task. This study introduces a graph-based deep learning framework that addresses the...
Brain imagingMagnetic resonance imaging
10.1038/S43856-026-01395-Y
ISSN:2730-664X

Towards a general-purpose foundation model for functional MRI analysis

Cheng WangYu JiangZhihao PengChenxin LiChang-bae Bang21
Nature Biomedical Engineering
2026
2026/4/23
00 p.1-12
Functional magnetic resonance imaging (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. Here we int...
Computational scienceMagnetic resonance imaging
10.1038/S41551-026-01666-Y
ISSN:2157-846X

Aberrant resting-state co-activation network dynamics in major depressive disorder

Ziqi AnKai TangYuanyao XieChuanjun TongJiaming Liu7
Translational Psychiatry
2024
2024/1/3
Vol.14 No.1 p.1-12
Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The ...
DepressionDiagnostic markers
10.1038/S41398-023-02722-W
ISSN:2158-3188

Transition and dynamic reconfiguration in late-life depression based on hidden Markov model

Hairong XiaoCaili KangWei ZhaoShuixia Guo
Npj Mental Health Research
2025
2025/5/27
Vol.4 No.1 p.1-11
Late-life depression is characterized by persistent emotional distress and cognitive dysfunction, yet understanding the specific brain dynamics and molecular mechanisms involved remains limited. Here, we employed a hidden Markov model to analyze resting-state functional magnetic resonance imaging da...
DepressionFunctional magnetic resonance imagingGene expression profilingMagnetic resonance imaging
10.1038/S44184-025-00137-7
ISSN:2731-4251

Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder

Yuan LiuMeijuan LiBin ZhangWen QinYing Gao7
Translational Psychiatry
2024
2024/8/31
Vol.14 No.1 p.1-8
Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by ana...
DepressionHuman behaviour
10.1038/S41398-024-03062-Z
ISSN:2158-3188

Beyond depression symptoms: the default mode network as a predictor of antidepressant response

Kaizhong ZhengLiangjun ChenHuaning WangBaojuan LiBadong Chen
Npj Mental Health Research
2026
2026/1/16
Vol.5 No.1 p.20
Antidepressant efficacy for major depressive disorder (MDD) remains limited, with the neural mechanisms underlying treatment response poorly understood. The default mode network (DMN), particularly the connectivity between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), has...
BiomarkersDiseasesNeurologyNeurosciencePsychology
10.1038/S44184-025-00182-2
ISSN:2731-4251

Modulation of suicide-related neural circuits by transcranial magnetic stimulation and its role in reducing suicide risk

Shiying WangChengfeng ChenJiang WangRu HaoYuan Liu7
Translational Psychiatry
2025
2025/12/10
0
Suicide remains a critical public health issue, frequently linked to dysfunction in specific neural circuits. Disruption of these circuits may contribute to the onset of suicide. Accordingly, this study investigates the therapeutic potential of transcranial magnetic stimulation (TMS) in modulating s...
DepressionNeuroscience
10.1038/S41398-025-03790-W
ISSN:2158-3188

Multilevel brain functional connectivity and task-based representations explaining heterogeneity in major depressive disorder

Qi LiuXinqi ZhouChunmei LanXiaolei XuYuanshu Chen12
Translational Psychiatry
2025
2025/6/13
Vol.15 No.1 p.1-12
Major depressive disorder (MDD) is a devastating mental disorder characterized by considerable clinical and biological heterogeneity. While comparable clinical symptoms may represent a common pathological endpoint, it is conceivable that distinct neurophysiological mechanisms underlie their manifest...
DepressionHuman behaviourNeuroscience
10.1038/S41398-025-03413-4
ISSN:2158-3188