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Multimodal graph isomorphism network to study fMRI connectivity in developmental stages of adolescence

A fundamental understanding of sex differences that exist in healthy individuals is critical for the study of neurological illnesses that exhibit phenotypic differences between both genders. Functional magnetic resonance imaging(fMRI) is a useful way to study this problem since it provides a non-invasive and high-resolution tool for observing the fluctuation in blood oxygenation level dependent (BOLD) signals to characterize the metabolism of the human brain. In the meantime, graph neural networks (GNNs) can be applied to fMRI data to effectively discover novel biomarkers underlying brain development. We propose a multi-modal graph isomorphism network (MGIN) to analyze the sex differences based on fMRI task data. Our method is able to integrate all the available connectivity data into graphs for deep learning, and it can be applied to multigraphs with different nodes to learn local graph information without binding to the entire graph. MGIN model can identify important subnetworks between and within multi-task data. In addition, it is interpretable by using GNNExplainer to provide important domain insights to identify graph structures and node features that contribute significantly to the classification results. Our MGIN model can achieve better classification accuracy compared to competing models. We applied the model to a cohort of brain development study to classify sex during different stages of adolescence and experimental results showed that our model can improve classification accuracy and help in our understanding of neurodevelopment during adolescence.