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

Abstract

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.