BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

By Xiaoxiao Li, Yuan Zhou, Siyuan Gao, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Shi Gu, Dustin Scheinost, Lawrence H. Staib, Pamela Ventola and 1 more. bioRxiv, 2020

802 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/aa9635e531de729d47c9f166c388cd5ff0feeaaf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohamed Ali Mahjoub
2. Lin Liu
3. Islem Rekik
4. Alaa Bessadok

In Cognitive Neuroscience

1. Mohamed Ali Mahjoub
2. Islem Rekik
3. Alaa Bessadok

In Radiology, Nuclear Medicine and Imaging

1. Mohamed Ali Mahjoub
2. Islem Rekik
3. Alaa Bessadok

Citations in the corpus

• Graph Neural Networks in Network Neuroscience (2021, background): “On the other hand, (78, 80) proposed a GAT-based architecture to predict the most discriminative ROIs for identifying a disease.”
• Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “…retrieval (Zhai et al., 2019), brain connection (Ktena et al., 2017, 2018; Li X. et al., 2019a; Mirakhorli and Mirakhorli, 2019; Grigis et al., 2020; Li et al., 2020; Zhang and Pierre, 2020; Zhang et al., 2021) and disease prediction (Parisot et al., 2017; Kazi et al., 2018, 2019a,b,c; Anirudh…”