Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

翻译：利用结构或功能连接对人体大脑连接进行绘图,已成为神经成像分析最普遍的范例之一。最近,以几何深学习为动机的图形神经网络(GNNs)因其在建模复杂网络数据方面的既定能力而吸引了广泛的兴趣。尽管它们在许多领域表现优异,但尚未对如何设计有效的GNNs进行脑网络分析的有效GNS进行系统研究。为了缩小这一差距,我们介绍了BenGB,这是与GNS进行脑网络分析的基准。BenGB将这一过程标准化,其方法是:(1) 总结功能和结构神经成型模式的脑网络建设管道,(2) 模块化GNN设计的实施。我们广泛试验了跨组群群和模式的数据集,并为脑网络的有效GNN设计推荐了一套一般的配方。为了支持对基于GNN的脑网络分析进行公开和可复制的研究,我们在 https://braingb.us 上开设了ChenGB网站, 并设有模型、导师、示例,以及外箱Python软件包中提供有用的经验证据和展望。我们希望这项工作将为未来研究提供有用的新方向。