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Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN).
- Source :
-
Journal of X-Ray Science & Technology . 2024, Vol. 32 Issue 4, p1041-1059. 19p. - Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08953996
- Volume :
- 32
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of X-Ray Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 179399695
- Full Text :
- https://doi.org/10.3233/XST-230426