Traditional deep learning architectures have met with limited performance improvement on fMRI data analysis. Our connectivity-based graph convolutional network modeled fMRI data as graphs and performed convolutions within connectivity-based neighborhood. We demonstrate that our approach is substantially more robust in classifying Autism Spectrum Disorder (ASD) patients from normal subjects compared with those in published work. Extracting spatial features and averaging across frames are beneficial in reducing variance and improving classification accuracy.
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