  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.
