Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

中文翻译：

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基于图深度学习的脑网络异常预测新方法[J].

功能性磁共振成像是一种用于脑部疾病和功能障碍研究的神经成像技术，近年来通过绘制大脑连接的拓扑结构（称为连接图映射）得到了改进。基于健康和不健康的大脑区域和功能略有不同的事实，考虑到评估措施的增长，研究人脑功能和结构网络的复杂拓扑结构过于复杂。不规则图深度学习的应用之一是分析与基因表达和相关分布空间模式相关的人类认知功能。由于各种大脑解决方案可以动态地保存在大脑的神经元网络中，具有不同的活动模式和功能连接，此应用程序涉及以节点为中心和以图形为中心的任务。在本研究中，我们对在执行某些任务和静止状态或分解不规则观察过程中具有异常连接的大脑感兴趣区域识别区域使用个体生成模型和高阶图分析。因此，本文提出了一种高斯分布的变分图自动编码器的高阶框架来分析脑成像研究中的功能数据，其中使用生成对抗网络优化学习强非刚性图过程中的潜在空间在大规模数据中。此外，在异常的大脑连接中可以区分可能的相关模式。我们的目标是找出受影响区域及其随时间同时发生的相关程度。我们可以利用这一点来诊断大脑疾病或展示神经系统根据输入刺激在各个角度修改大脑拓扑结构和大脑可塑性的能力。在这项研究中，我们特别关注阿尔茨海默病。