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Related Concept Videos

Depressive Disorders: MDD and Dysthymia01:27

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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...
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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Using dynamic graph convolutional network to identify individuals with major depression disorder.

Ni Zhou1, Ze Yuan2, Hongying Zhou3

  • 1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Hongkou Mental Health Center, Shanghai, China.

Journal of Affective Disorders
|November 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Dynamic Graph Convolutional Nets (DGCNs) for diagnosing major depressive disorder (MDD) using brain scans. The novel approach achieved 82.5% accuracy, improving upon previous machine learning methods for MDD detection.

Keywords:
Dynamic graph neural networkMachine learningMagnetic resonance imagingMajor depression disorderMulti-site

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Psychiatry

Background:

  • Objective neuroimaging biomarkers are vital for early major depressive disorder (MDD) diagnosis.
  • Previous machine learning (ML) studies often lacked large sample sizes and overlooked neural connectome mechanisms in MDD.

Purpose of the Study:

  • To apply Dynamic Graph Convolutional Nets (DGCNs) to a large, multi-site dataset for improved MDD diagnosis.
  • To investigate the utility of whole-brain functional connectivity (FC) networks in classifying MDD.
  • To enhance neurobiological understanding of MDD through advanced ML techniques.

Main Methods:

  • Utilized resting-state functional MRI (RS-fMRI) data from 1081 MDD patients and 1236 healthy controls across 16 sites.
  • Applied Dynamic Graph Convolutional Nets (DGCNs) to analyze personal whole-brain functional connectivity (FC) networks.
  • Compared DGCN performance against other universal ML classifiers.

Main Results:

  • The DGCN model achieved 82.5% accuracy (AUC:0.869) in distinguishing MDD patients from healthy controls.
  • DGCN performance surpassed that of other universal ML classifiers.
  • Key brain network domains for classification included the default mode, fronto-parietal, and cingulo-opercular networks.

Conclusions:

  • The study validates the stability and efficacy of DGCNs for characterizing MDD.
  • DGCNs show potential for advancing neurobiological comprehension of MDD.
  • This method may aid in detecting clinically relevant disorders within FC network topologies.