Structural- and Functional-Connectivity Convolution Neural Networks (SCFCnn) for Integrated Brain-Behavior Prediction in the HCP dataset

In this work, we investigate an efficient structural (SC)- and functional (FC)-connectivity convolution neural network (SCFCnn) architecture applied on both FC and SC to detect the links between individual non-imaging language traits and invivo MRI measurements in a subset of the Human Connectome Project (HCP) s900 dataset. The identified structure-function relationships can be used to infer neurocognitive measures from neuroimaging. Our new architecture outperforms popular deep learning neural networks, confirming the importance of convolutional neural networks applied to brain connectivity for better predictive performance in neurocognitive measurements.