Functional connectivity (FC) between regions of the brain can be assessed by
the degree of temporal correlation measured with functional neuroimaging
modalities. Based on the fact that these connectivities build a network,
graph-based approaches for analyzing the brain connectome have provided
insights into the functions of the human brain. The development of graph neural
networks (GNNs) capable of learning representation from graph structured data
has led to increased interest in learning the graph representation of the brain
connectome. Although recent attempts to apply GNN to the FC network have shown
promising results, there is still a common limitation that they usually do not
incorporate the dynamic characteristics of the FC network which fluctuates over
time. In addition, a few studies that have attempted to use dynamic FC as an
input for the GNN reported a reduction in performance compared to static FC
methods, and did not provide temporal explainability. Here, we propose STAGIN,
a method for learning dynamic graph representation of the brain connectome with
spatio-temporal attention. Specifically, a temporal sequence of brain graphs is
input to the STAGIN to obtain the dynamic graph representation, while novel
READOUT functions and the Transformer encoder provide spatial and temporal
explainability with attention, respectively. Experiments on the HCP-Rest and
the HCP-Task datasets demonstrate exceptional performance of our proposed
method. Analysis of the spatio-temporal attention also provide concurrent
interpretation with the neuroscientific knowledge, which further validates our
method. Code is available at this https URL

提出了一种名为 STAGIN 的方法，该方法可以学习到大脑连通性的动态图表示，其中包括空间 - 时间关注的新型读取功能和变压器编码器，对 HCP - 休息和 HCP - 任务数据集的实验表现优异，并且具有与神经科学知识的相应解释。

使用时空注意力学习脑连通图的动态图表示

Learning Dynamic Graph Representation of Brain Connectome with  Spatio-Temporal Attention

The generalization ability of Convolutional neural networks (CNNs) for
biometrics drops greatly due to the adverse effects of various occlusions. To
this end, we propose a novel unified framework integrated the merits of both
CNNs and graphical models to learn dynamic graph representations for occlusion
problems in biometrics, called Dynamic Graph Representation (DGR).
Convolutional features onto certain regions are re-crafted by a graph generator
to establish the connections among the spatial parts of biometrics and build
Feature Graphs based on these node representations. Each node of Feature Graphs
corresponds to a specific part of the input image and the edges express the
spatial relationships between parts. By analyzing the similarities between the
nodes, the framework is able to adaptively remove the nodes representing the
occluded parts. During dynamic graph matching, we propose a novel strategy to
measure the distances of both nodes and adjacent matrixes. In this way, the
proposed method is more convincing than CNNs-based methods because the dynamic
graph method implies a more illustrative and reasonable inference of the
biometrics decision. Experiments conducted on iris and face demonstrate the
superiority of the proposed framework, which boosts the accuracy of occluded
biometrics recognition by a large margin comparing with baseline methods.The
code is avaliable at
this https URL

本篇研究提出了一种称为动态图表示（DGR）的新颖统一框架，整合了卷积神经网络（CNNs）和图模型的优点，用于学习生物识别中的遮挡问题，其中通过分析节点间的相似性，能够自适应地移除代表被遮挡部分的节点，实验结果表明该方法优于基线方法并可大幅提高对遮挡生物识别的准确性。