Abstract
Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. In contrast to feedforward neural networks (FNN) and convolutional neural networks (CNN) in traditional functional connectivity-based fMRI analysis methods, we construct weighted graphs from fMRI and apply a GNN to fMRI brain graphs. Considering the special property of brain graphs, we design novel brain ROI-aware graph convolutional layers (Ra-GNN) that leverages the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms – unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss – on pooling results to encourage reasonable ROI-selection and provide flexibility to preserve either individualor group-level patterns. We apply the BrainGNN framework on two independent fMRI datasets: Autism Spectral Disorder (ASD) fMRI dataset and Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyperparameters and show that BrainGNN outperforms the alternative FNN, CNN and GNN-based fMRI image analysis methods in terms of classification accuracy. The obtained community clustering and salient ROI detection results show high correspondence with the previous neuroimagingderived evidence of biomarkers for ASD and specific task states decoded in task-fMRI.
Competing Interest Statement
The authors have declared no competing interest.
