利用区分性脑子网络筛查自闭症谱系障碍：熵方法
Autism Spectrum Disorder Screening Using Discriminative Brain Sub-Networks: An Entropic Approach
摘要：
自闭症是最重要的神经系统疾病之一，它会导致一个人的社会交往问题。脑成像技术的进步有助于我们构建脑的结构和功能网络。在每个组（自闭症和健康对照组）中找到网络拓扑模式可以帮助我们实现自闭症障碍筛查模型。在本研究中，我们利用遗传算法提取一个判别子网络，更好地代表两组之间的差异。在适应度评估阶段，对于每个子网络，使用子网络的各种熵特征训练机器学习模型，并测量其性能。适当的模型性能意味着提取一个良好的判别子网络。网络熵可用作网络拓扑描述符。评估结果表明，所提出的筛选方法的基础上提取的判别子网络和机器学习模型成功地获得了73.1%的最大准确率在UCLA数据集的结构网络，82.2%的功能网络的UCLA数据集，和66.1%的ABIDE数据集的功能网络的可接受的性能。
英文摘要：
Autism is one of the most important neurological disorders which leads to problems in a person's social interactions. Improvement of brain imaging technologies and techniques help us to build brain structural and functional networks. Finding networks topology pattern in each of the groups (autism and healthy control) can aid us to achieve an autism disorder screening model. In the present study, we have utilized the genetic algorithm to extract a discriminative sub-network that represents differences between two groups better. In the fitness evaluation phase, for each sub-network, a machine learning model was trained using various entropy features of the sub-network and its performance was measured. Proper model performance implies extracting a good discriminative sub-network. Network entropies can be used as network topological descriptors. The evaluation results indicate the acceptable performance of the proposed screening method based on extracted discriminative sub-networks and the machine learning models succeeded in obtaining a maximum accuracy of 73.1% in structural networks of the UCLA dataset, 82.2% in functional networks of the UCLA dataset, and 66.1% in functional networks of ABIDE datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
