Structural- and Functional-Connectivity Convolution Neural Networks (SCFCnn) for Integrated Brain-Behavior Prediction in the HCP dataset
Ying-Chia Lin1,2, Steven Baete1,2, Xiuyuan Wang1,2, and Fernando Boada1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States
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.
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