Connectome based schizophrenia prediction using structural connectivity - Deep Graph Neural Network (sc-DGNN)

by, Udayakumar.P and Dr.R.Subhashini [Paper] Vellore Institute of Technology, Vellore, India

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Background:Connectome is understanding the complex organization of the human brain’s structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. Objective:To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. Method:By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. Result:The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). Conclusion:The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.

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@article{udayakumar2024connectome,
  title={Connectome-based schizophrenia prediction using structural connectivity-Deep Graph Neural Network (sc-DGNN)},
  author={Udayakumar, P and Subhashini, R},
  journal={Journal of X-Ray Science and Technology},
  number={Preprint},
  pages={1--19},
  year={2024},
  publisher={IOS Press}
}