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Objective: Multi-modal functional magnetic resonance imaging (fMRI) can beused to make predictions about individual behavioral and cognitive traits basedon brain connectivity networks. Methods: To take advantage of complementaryinformation from multi-modal fMRI, we propose an interpretable multi-modalgraph convolutional network (MGCN) model, incorporating the fMRI time seriesand the functional connectivity (FC) between each pair of brain regions.Specifically, our model learns a graph embedding from individual brain networksderived from multi-modal data. A manifold-based regularization term is thenenforced to consider the relationships of subjects both within and betweenmodalities. Furthermore, we propose the gradient-weighted regression activationmapping (Grad-RAM) and the edge mask learning to interpret the model, which isused to identify significant cognition-related biomarkers. Results: We validateour MGCN model on the Philadelphia Neurodevelopmental Cohort to predictindividual wide range achievement test (WRAT) score. Our model obtains superiorpredictive performance over GCN with a single modality and other competingapproaches. The identified biomarkers are cross-validated from differentapproaches. Conclusion and Significance: This paper develops a newinterpretable graph deep learning framework for cognitive ability prediction,with the potential to overcome the limitations of several current data-fusionmodels. The results demonstrate the power of MGCN in analyzing multi-modal fMRIand discovering significant biomarkers for human brain studies.

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction