Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depressive Disorders: MDD and Dysthymia

Depressive Disorders: MDD and Dysthymia

Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Extreme heat and mental health: systematic review and qualitative investigation of risk and protective factors.

Psychological medicine·2026

Same author

Associations between birdsong environments and momentary human affective wellbeing: evidence from real-world.

Environment international·2026

Same author

Stressful life events and their association with symptom severity and functioning in first-episode psychosis.

Schizophrenia (Heidelberg, Germany)·2026

Same author

Treatment-related topological reorganization of the structural connectome in major depressive disorder with suicidal ideation.

BMC psychiatry·2026

Same author

Urinary copper is linked to regional cortical volume reductions in adolescents with major depressive disorder.

Translational psychiatry·2026

Same author

The impact of childhood trauma on brain structural connectivity in adolescent major depressive disorder: A graph-based analysis.

Journal of affective disorders·2026

Search research articles

Related Experiment Video

Updated: Sep 28, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging

Kun Qin¹, Du Lei², Walter H L Pinaya³

¹Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.

|April 3, 2022

Summary

Graph convolutional networks (GCNs) show promise for diagnosing major depressive disorder (MDD) by analyzing brain connectivity. This advanced machine learning approach achieved high accuracy in a large, multi-site study, identifying key brain regions involved in MDD.

Keywords:

Deep learning Depression Graph neural network Graph theory Magnetic resonance imaging Multi-site

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

Related Experiment Videos

Last Updated: Sep 28, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

Area of Science:

Neuroimaging
Machine Learning
Psychiatry

Background:

Objective biomarkers are crucial for early major depressive disorder (MDD) diagnosis.
Previous machine learning studies for MDD often used small sample sizes and overlooked brain connectome.
This study addresses these limitations using a large, multi-site dataset and graph convolutional networks (GCNs).

Purpose of the Study:

To apply GCNs to a large, multi-site MDD dataset for improved diagnostic accuracy.
To identify brain regions critical for MDD classification.
To explore the relationship between brain network topology and clinical features of MDD.

Main Methods:

Utilized resting-state functional MRI data from 1586 participants (821 MDD, 765 controls) across 16 sites.
Trained a GCN model on individual whole-brain functional networks to differentiate MDD patients from controls.
Analyzed salient regions and their topological characteristics in relation to clinical measures.

Main Results:

GCN achieved 81.5% accuracy (AUC: 0.865), outperforming other classifiers.
Key regions identified were within the default mode, fronto-parietal, and cingulo-opercular networks.
Left inferior parietal lobule and left dorsolateral prefrontal cortex topology correlated with depressive severity and illness duration.

Conclusions:

GCN is a feasible and effective tool for characterizing MDD in large, multi-site studies.
GCN enhances understanding of MDD neurobiology by detecting functional network disruptions.
This approach holds potential for improving MDD diagnosis and treatment strategies.