Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 12; pp. 3564 - 3573
• Main Authors: Qu, Gang, Xiao, Li, Hu, Wenxing, Wang, Junqi, Zhang, Kun, Calhoun, Vince, Wang, Yu-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Achievement tests; Artificial neural networks; Biological system modeling; Biomarkers; Brain; Brain mapping; Brain modeling; Cognition; Cognition & reasoning; Cognitive ability; Data integration; Deep learning; Embedding; fMRI; functional connectivity; Functional magnetic resonance imaging; graph convolutional networks; interpretable deep learning; Magnetic resonance imaging; Manifolds; Modal data; multi-modal deep learning; Neural networks; Neuroimaging; Performance prediction; Predictions; Predictive models; Regularization; Time series analysis
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3077875

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers. Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches. Conclusion and Significance: This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.