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.
This abstract and the presentation materials are available to members only; a login is required.