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3DViT-GAT: a unified atlas-based 3D vision transformer and graph learning framework for major depressive disorder detection using structural MRI data.

Scientific reports·2026
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Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.

Nojod M Alotaibi1, Areej M Alhothali1, Manar S Ali1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in Computational Neuroscience
|June 24, 2025
PubMed
Summary

This study introduces a novel ensemble graph neural network (GNN) model using functional MRI data to improve major depressive disorder (MDD) detection. The model shows promise for accurate and generalizable diagnosis of MDD.

Keywords:
RS-fMRIbrain functional connectivity networkdata oversamplingdeep learningensemble modelgraph neural networkmajor depressive disorder

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Major Depressive Disorder (MDD) is a leading cause of disability, often diagnosed through subjective symptoms, overlooking complex pathophysiology.
  • Current diagnostic methods for MDD lack objective measures, highlighting the need for advanced techniques to understand its brain network disorder nature.
  • Neuroimaging, particularly rest-state functional MRI (rs-fMRI), is crucial for investigating brain alterations in MDD.

Purpose of the Study:

  • To develop an ensemble-based Graph Neural Network (GNN) model for enhanced detection of Major Depressive Disorder (MDD) using rs-fMRI data.
  • To leverage multi-atlas functional connectivity features to capture brain complexity and improve diagnostic accuracy for MDD.
  • To validate the model's generalizability and reliability on a large, multi-site MDD dataset.

Main Methods:

  • Utilized rest-state functional Magnetic Resonance Imaging (rs-fMRI) data from a large, multi-site cohort diagnosed with MDD.
  • Developed an ensemble Graph Neural Network (GNN) model integrating functional connectivity derived from multiple brain region segmentation atlases.
  • Applied Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and employed stratified 10-fold cross-validation for performance assessment.

Main Results:

  • The proposed multi-atlas ensemble GNN model achieved a high accuracy of 75.80% in detecting MDD.
  • Key performance metrics included sensitivity of 88.89%, specificity of 61.84%, precision of 71.29%, and an F1-score of 79.12%.
  • The ensemble approach demonstrated superior performance compared to single-atlas models in capturing discriminative features for MDD diagnosis.

Conclusions:

  • The developed ensemble GNN model offers a reliable and generalizable approach for the accurate detection of MDD from rs-fMRI data.
  • This study underscores the potential of deep learning techniques, specifically GNNs, in advancing the objective diagnosis of mental health disorders.
  • Integrating multi-atlas functional connectivity enhances the model's ability to identify complex brain network alterations characteristic of MDD.