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Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary

Shurun Wang1, Hao Tang2, Ryutaro Himeno3

  • 1School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan.

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Summary
This summary is machine-generated.

This study introduces an evolutionary algorithm-based graph neural network for diagnosing schizophrenia spectrum disorder, outperforming traditional methods with high accuracy and providing explainable AI insights for better patient care.

Keywords:
Brain functional connectivityEvolutionary algorithmGraph neural architecture searchGraph neural networkSchizophrenia spectrum disorder

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate diagnosis of schizophrenia spectrum disorder is crucial for patient outcomes.
  • Functional connectivity analysis using fMRI offers potential biomarkers for diagnosis.
  • Existing methods may not fully capture complex spatial relationships in brain data.

Purpose of the Study:

  • To develop an advanced method for schizophrenia spectrum disorder diagnosis.
  • To improve the accuracy and interpretability of diagnostic models.
  • To leverage graph neural networks for enhanced brain connectivity analysis.

Main Methods:

  • Proposed an evolutionary algorithm-based graph neural architecture search (EA-GNAS).
  • Utilized GNNExplainer for model interpretability.
  • Applied the method to a multi-site dataset of schizophrenia spectrum disorder patients.

Main Results:

  • The EA-GNAS model achieved superior performance compared to conventional and deep learning approaches.
  • Achieved high accuracy (0.8246), F1 score (0.8438), and AUC (0.8258) in cross-validation.
  • Demonstrated the model's ability to provide accurate and comprehensible predictions.

Conclusions:

  • The developed graph neural network model enhances schizophrenia spectrum disorder diagnosis.
  • Findings advance the understanding of brain function in schizophrenia spectrum disorder.
  • The method shows potential as a novel biomarker for diagnosing schizophrenia spectrum disorder.