图卷积网络和功能连接在自闭症谱系障碍识别中的应用

Hichem Felouat, Saliha Oukid-Khouas
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引用次数: 3

摘要

本研究的目的是应用图卷积网络(GCNs)对自闭症谱系障碍(ASD)患者进行特征提取和分类。自闭症患者的数量每年都在增加,并对许多儿童的生活和未来构成威胁,因此这项研究非常重要。我们使用来自大型多站点数据集(称为自闭症脑成像数据交换I (ABIDE I))的静息状态fMRI数据来验证我们提出的方法。基于功能连通性(FC),我们通过一个复杂的网络来表示大脑,其中大脑的区域代表网络中的节点,两个区域之间的相关系数代表连接它们的边的权重。对数据进行预处理,并使用(CC400)图谱将整个大脑划分为392个不同的区域,为每个受试者构建了功能连接图。然后计算图测度,并将其作为节点和边缘的特征,通过本文提出的图卷积网络分类器对这些主题进行分类。我们在实验中取得的结果是,从健康个体中识别自闭症谱系障碍患者的准确率为70%,这证明了我们的方法在脑部疾病分类方面的准确性和稳健性。
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Graph Convolutional Networks and Functional Connectivity for Identification of Autism Spectrum Disorder
The purpose of this study is to apply graph convolutional networks (GCNs) for feature extraction and classification of patients with autism spectrum disorder (ASD). The number of people with (ASD) increases every year and poses a threat to the life and future of many children which makes this study very important. We used the resting-state fMRI data from a large multi-site dataset called Autism Brain Imaging Data Exchange I (ABIDE I) to validate our proposed approach. Based on functional connectivity (FC), we represented the brain through a complex network where the regions of the brain represent the nodes in the network and the correlation coefficient between two regions represents the weight of the edge connects them. The data were preprocessed, and we constructed a functional connectivity graph for each subject by parcellation of the whole brain into 392 distinct regions using the (CC400) atlas. The graph measures were then calculated and used as features for both nodes and edges to classify these subjects by graph convolutional networks’ classifier which proposed in this study. The results we achieved in our experiments were with accuracy of 70% to identify patients with autism spectrum disorder from healthy individuals, which proved the accuracy and robustness of our approach in classifying brain diseases.
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