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Abstract #0572

Resting-state fMRI Predicts Task Activation Patterns Using a Graph Convolutional Network

Zhangxuan Hu1,2, Hua Guo2, Lihong Wang3, Bing Wu1, and Xue Zhang4
1GE Healthcare, MR Research China, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3Department of Psychiatry, University of Connecticut School of Medicine, Farmington, MI, United States, 4Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States

Resting-state fMRI has the clinical potential as diagnostic and prognostic markers because of its easy implementation/standardization in data acquisitions, and its ability to parcellate functionally connected neural networks. It is of importance to examine whether the task-free spontaneous activity could be used to predict individuals’ task-induced activation. Here we proposed a graph convolutional network-based framework which utilized the information of the brain connections for the convolution step, and showed the ability of using resting-state fMRI to predict individual differences in activations of tasks from human connectome project. This framework could be extended to other resting-state fMRI researches.

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