Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, with a particular emphasis on the robustness of biomarkers produced for neurodegenerative diseases and neuropsychiatric disorders. We found that while most studies have performant models, salient features highlighted in these studies vary greatly across studies on the same disorder and little has been done to evaluate their robustness. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on improving the robustness of potential biomarkers discovered via GNNs.

翻译：图神经网络（GNN）已成为建模功能性磁共振成像（fMRI）数据集的主流工具。近期诸多研究通过更精妙的GNN设计显著提升了疾病分类性能，并突出显示了可作为疾病潜在生物标志物的显著特征。本综述系统阐述了GNN及模型可解释性技术如何应用于基于fMRI数据集的疾病预测任务，特别关注针对神经退行性疾病及神经精神疾病所生成生物标志物的鲁棒性。我们发现：尽管多数研究构建了高性能模型，但针对同一疾病的不同研究所强调的显著特征差异巨大，且鲜有研究评估其鲁棒性。为解决这些问题，我们建议建立基于客观评价指标的新标准来判定这些潜在生物标志物的鲁棒性。此外，我们指出现有文献中的研究空白，并构建了一个预测-归因-评估框架，为未来通过GNN发现鲁棒性潜在生物标志物的研究奠定基础。