论文标题
修剪图卷积网络以选择fMRI解码的有意义的图形频率
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
论文作者
论文摘要
图形信号处理是操纵大脑信号的有前途的框架，因为它允许涵盖大脑感兴趣区域的活动之间的空间依赖性。在这项工作中，我们有兴趣更好地理解用于解码fMRI信号最有用的图形频率。为此，我们引入了深度学习体系结构，并适应修剪方法，以自动识别此类频率。我们尝试各种数据集，架构和图形，并表明低图频率始终被识别为fMRI解码最重要的频率，并且在结构图上对功能图的贡献更强。我们认为，这项工作提供了有关如何部署基于图的方法以提高fMRI解码准确性和解释性的新见解。
Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one. We believe that this work provides novel insights on how graph-based methods can be deployed to increase fMRI decoding accuracy and interpretability.
