Abstract #3908

Recurrent Edge-Convolutional Neural Network for Individual Identification Using Resting-state fMRI Data

Lebo Wang¹, Kaiming Li², Xu Chen³, and Xiaoping Hu^1,2,3

¹Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, United States, ²Department of Bioengineering, University of California, Riverside, Riverside, CA, United States, ³Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States

Convolutional neural networks extract local features effectively on volumetric data. For the ROI-based fMRI data, we proposed the recurrent edge-convolutional neural network to model spatial coactivation pattern and dynamics. Edge-convolution could depict the relation between hyper-neighborhoods based on pre-computed functional connectivity and corresponding graph of k-nearest neighbors. Our proposed model demonstrated consistently better accuracy to identify 100 subjects from the Human Connectome Project. Especially with limited number of frames, our proposed model could achieve highest identification accuracy, where stable extraction of spatial features is maintained under high temporal variation.

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