  Finding the biomarkers associated with ASD is helpful for understanding the
underlying roots of the disorder and can lead to earlier diagnosis and more
targeted treatment. A promising approach to identify biomarkers is using Graph
Neural Networks (GNNs), which can be used to analyze graph structured data,
i.e. brain networks constructed by fMRI. One way to interpret important
features is through looking at how the classification probability changes if
the features are occluded or replaced. The major limitation of this approach is
that replacing values may change the distribution of the data and lead to
serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need
to replace features for reliable biomarker interpretation. Specifically, we
propose an inductive GNN to embed the graphs containing different properties of
task-fMRI for identifying ASD and then discover the brain regions/sub-graphs
used as evidence for the GNN classifier. We first show GNN can achieve high
accuracy in identifying ASD. Next, we calculate the feature importance scores
using GNN and compare the interpretation ability with Random Forest. Finally,
we run with different atlases and parameters, proving the robustness of the
proposed method. The detected biomarkers reveal their association with social
behaviors. We also show the potential of discovering new informative
biomarkers. Our pipeline can be generalized to other graph feature importance
interpretation problems.
