BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

• Published in: bioRxiv
• Main Authors: Li, Xiaoxiao, Zhou, Yuan, Gao, Siyuan, Dvornek, Nicha, Zhang, Muhan, Zhuang, Juntang, Gu, Shi, Scheinost, Dustin, Staib, Lawrence, Ventola, Pamela, Duncan, James
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 23.10.2020; Cold Spring Harbor Laboratory
• Edition: 1.4
• Subjects: Autism; Biomarkers; Brain mapping; Cognitive ability; Functional magnetic resonance imaging; Image processing; Medical imaging; Neural networks; Neuroimaging; Neuroscience
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2020.05.16.100057

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. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage 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 individual- or 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 fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Competing Interest Statement The authors have declared no competing interest. Footnotes * Updated experimental results and polished writing.