Prediction of Cognitive Scores by Movie-Watching FMRI Connectivity and Eye Movement Via Spectral Graph Convolutions

Abstract

Brain functional connectivity has been demonstrated to serve as a "fingerprint" to predict individual behaviors and phenotypes. A precise mapping between them could provide insightful clues to brain architectures and the generation of cognition. In this context, the naturalistic paradigm provides more engaging conditions and richer fMRI information, and both preserves or even enhances individual features and increases sensitivity to phenotypic measures, compared with other functional MRI modalities including resting-state and task paradigms. However, to the best of our knowledge, only linear methods were developed for predicting phenotypic measures from brain activity under naturalistic stimulus, while the brain activity is highly dynamic and nonlinear. Hence, we adopted the nonlinear graph convolutional network (GCN) to predict cognition-related phenotypic score from brain functional connectivity under naturalistic stimulus, where subjects are the nodes and functional connectivity is node feature. The behavior patterns of eye movement were integrated into this method to estimate similarity across subjects and define the graph edges. A few nodes are labeled by their phenotypic score, and the model is trained to predict the scores of those unlabeled nodes. The prediction accuracy of this method outperforms those from the linear classification method, resting-state based functional node feature and random edge tests.