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Graph Neural Networks (GNNs) have been shown to be a powerful tool forgenerating predictions from biological data. Their application to neuroimagingdata such as functional magnetic resonance imaging (fMRI) scans has beenlimited. However, applying GNNs to fMRI scans may substantially improvepredictive accuracy and could be used to inform clinical diagnosis in thefuture. In this paper, we present a novel approach to representingresting-state fMRI data as a graph containing nodes and edges without omittingany of the voxels and thus reducing information loss. We compare multiple GNNarchitectures and show that they can successfully predict the disease and sexof a person. We hope to provide a basis for future work to exploit the power ofGNNs when applied to brain imaging data.

Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans