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Abstract #3687

Connectivity-based Graph Convolutional Network for fMRI Data Analysis

Lebo Wang1, Kaiming Li2, and Xiaoping Hu1,2
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

Graphs have been widely applied for ROI-based fMRI data analysis, in which the functional connectivity (FC) between all pairs of regions is thoroughly considered. Combined with convolutional neural networks, we define graphs based on FC and introduce a connectivity-based graph convolution network (cGCN) architecture for fMRI data analysis. cGCN allows us to extract spatial features within connectivity-based neighborhood for each frame and capture the temporal dynamics between frames. Our results indicate that cGCN outperforms traditional deep learning architectures on fMRI data analysis.

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