Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a lass-onsistency and ite-ndependence ultiview yperedge-ware yperraph mbedding earning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

中文翻译：

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多视图超边缘感知超图嵌入学习，用于基于多站点、多图谱 fMRI 的功能连接网络分析

最近，基于功能磁共振成像（fMRI）的功能连接网络（FCN）通过图卷积网络（GCN）进行分析，通过将FCN视为不规则的图结构数据，显示出自动诊断脑部疾病的前景。然而，在多站点、多图集功能磁共振成像场景中，FCN 的多视图信息和站点影响尚未得到充分研究。在本文中，我们提出了一种类一致性和项独立性 ultiview yperedge-ware yperraph 嵌入收益 (CcSi-MHAHGEL) 框架，以将在多站点 fMRI 研究中的多个大脑图谱上构建的 FCN 集成起来。具体来说，对于每个主题，我们首先将大脑网络建模为每个大脑图谱的超图，以表征多个顶点之间的高阶关系，然后引入多视图超边缘感知超图卷积网络（HGCN）来提取基于多图谱的 FCN 嵌入其中超边缘权重是自适应学习的，而不是采用传统 HGCN 中预先计算的固定权重。此外，我们通过分别考虑跨类和站点之间的主题关联，制定了两个模块来共同学习基于多图集的 FCN 嵌入，即类一致性模块，以鼓励每个类内的紧凑性和类之间的分离，以促进嵌入空间中的歧视，以及站点独立模块，以最大限度地减少嵌入的站点依赖性，以减轻由于多个站点的扫描平台和/或协议的差异而产生的不良站点影响。最后，基于多图集的 FCN 嵌入被输入到几个完全连接的层中，然后是用于诊断决策的 soft-max 分类器。 ABIDE 上的大量实验证明了我们的自闭症谱系障碍 (ASD) 识别方法的有效性。此外，我们的方法可以通过揭示具有生物学意义的 ASD 相关大脑区域来解释。