Affiliations:
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
Correspondence:
[*]
Corresponding author: R Subhashini, School of Computer Science Engineering and Information Systems, 6 Vellore Institute of Technology, Vellore, Tamilnadu, India. E-mail rsubhashini@vit.ac.in.
Note: [1] These authors contributed equally to this work.
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.
Keywords: Connectome, brain disorder, neural network, graph measure, connectivity matrices, neuroimaging, tau protein
