Abstract：
UNASSIGNED: 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.
UNASSIGNED: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
摘要：
■Connectome了解人类大脑的结构和功能连接的复杂组织对于获得认知过程和障碍的见解至关重要。
■为了提高脑部疾病的预测准确性，本研究调查了与精神分裂症相关的连接不良子网络和图结构。
■通过使用提出的结构连通性-深层图神经网络（sc-DGNN）模型，并与机器学习（ML）和深度学习（DL）模型进行比较。这项工作试图集中在88个受试者的扩散磁共振成像（dMRI），三个经典ML，和五个DL模型。
■提出了结构连通性深度图神经网络（sc-DGNN）模型，以有效地预测与精神分裂症相关的连通性异常，并且与传统的ML和DL（GNN）方法相比，在准确性方面表现出卓越的性能，灵敏度,特异性，精度,F1分数，和接收机工作特性下面积(AUC)。
■使用结构连接矩阵和实验结果表明，线性判别分析（LDA）在ML模型中的准确率为72％，sc-DGNN在DL模型中以93％的准确率进行区分精神分裂症和健康患者。
■为了提高脑部疾病的预测准确性，本研究调查了与精神分裂症相关的连接不良子网络和图结构。
■通过使用提出的结构连通性-深层图神经网络（sc-DGNN）模型，并与机器学习（ML）和深度学习（DL）模型进行比较。这项工作试图集中在88个受试者的扩散磁共振成像（dMRI），三个经典ML，和五个DL模型。
■提出了结构连通性深度图神经网络（sc-DGNN）模型，以有效地预测与精神分裂症相关的连通性异常，并且与传统的ML和DL（GNN）方法相比，在准确性方面表现出卓越的性能，灵敏度,特异性，精度,F1分数，和接收机工作特性下面积(AUC)。
■使用结构连接矩阵和实验结果表明，线性判别分析（LDA）在ML模型中的准确率为72％，sc-DGNN在DL模型中以93％的准确率进行区分精神分裂症和健康患者。
