论文标题
通过多模式关节图表来推断大脑动力学EEG-FMRI
Inferring Brain Dynamics via Multimodal Joint Graph Representation EEG-fMRI
论文作者
论文摘要
最近的研究表明，多模型方法可以为分析脑部成分的分析提供新的见解，而这些分析是在单独获取每种模态时不可能的。不同模态的联合表示是一个强大的模型，用于分析同时获得的脑电图和功能磁共振成像（EEG-FMRI）。精确仪器的进步使我们能够通过非侵入性神经成像技术（例如EEG＆fMRI）观察人脑的时空神经动力学。流的非线性融合方法可以在时间和空间的不同维度中提取有效的大脑成分。基于图的分析与大脑结构具有许多相似之处，可以克服大脑映射分析的复杂性。在整个过程中，我们概述了几种不同媒体的相关性，从一个来源转移了基于图和深度学习方法。确定重叠可以为诊断神经可塑性研究的功能变化提供新的观点。
Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. The joint representations of different modalities is a robust model to analyze simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI). Advances in precision instruments have given us the ability to observe the spatiotemporal neural dynamics of the human brain through non-invasive neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams can extract effective brain components in different dimensions of temporal and spatial. Graph-based analyzes, which have many similarities to brain structure, can overcome the complexities of brain mapping analysis. Throughout, we outline the correlations of several different media in time shifts from one source with graph-based and deep learning methods. Determining overlaps can provide a new perspective for diagnosing functional changes in neuroplasticity studies.
