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Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...

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An Interpretable Functional-Dynamic Synaptic Graph Neural Network for Major Depressive Disorder Diagnosis from

Zhihong Chen1, Jiayi Peng2, Xiaorui Han3

  • 1The Clinical Hospital of Chengdu Brain Science Institute, Sichuan Institute for Brain Science and Brain-Inspired Intelligence, China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China.

International Journal of Neural Systems
|February 28, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, the functional-dynamic synaptic graph neural network (FDSyn-GNN), improves the prediction of major depressive disorder (MDD) by analyzing dynamic brain signals and connections. This approach offers potential for discovering new biomarkers for MDD.

Keywords:
Major depressive disorderdynamic encodinggraph neural networkresting-state fMRIsynaptic graph transformer

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychiatry

Background:

  • Major depressive disorder (MDD) impacts millions globally, necessitating improved diagnostic and monitoring tools.
  • Current resting-state functional magnetic resonance imaging (rs-fMRI) and deep learning methods for MDD prediction often neglect dynamic brain signal characteristics and inter-regional connection strengths.
  • This limitation leads to brain region updates lacking biological specificity in predictive models.

Purpose of the Study:

  • To introduce a novel deep learning framework, the functional-dynamic synaptic graph neural network (FDSyn-GNN), for enhanced prediction of major depressive disorder (MDD).
  • To address the limitations of existing methods by incorporating dynamic temporal features of blood oxygen level-dependent (BOLD) signals and connection strengths between brain regions.
  • To explore the potential of FDSyn-GNN for biomarker discovery and understanding the neural underpinnings of MDD.

Main Methods:

  • Development of the functional-dynamic synaptic graph neural network (FDSyn-GNN) model.
  • Integration of a bidirectional gated recurrent unit (Bi-GRU) timestamp encoding (BGTE) module to capture dynamic BOLD signal characteristics.
  • Incorporation of a synaptic graph Transformer (SGT) module for connection-aware updates of brain regions.

Main Results:

  • FDSyn-GNN demonstrated superior performance compared to 12 state-of-the-art (SOTA) baseline methods on two large-scale, multi-site MDD datasets.
  • Ablation studies confirmed the effectiveness of the integrated BGTE and SGT modules within the FDSyn-GNN framework.
  • Interpretability analyses suggested the model's potential for identifying novel biomarkers associated with MDD.

Conclusions:

  • The proposed FDSyn-GNN offers a significant advancement in leveraging dynamic rs-fMRI data for MDD prediction.
  • The model's ability to integrate temporal dynamics and network connectivity provides a more biologically specific approach to analyzing brain function in MDD.
  • FDSyn-GNN shows promise for biomarker discovery and offers valuable insights into the neurobiological mechanisms of major depressive disorder.