Spatio-Temporal Graph Convolution for Functional MRI Analysis

The BOLD signal of resting-state fMRI (rs-fMRI) records the functional brain connectivity in a rich dynamic spatio-temporal setting. However, existing methods applied to rs-fMRI often fail to consider both spatial and temporal characteristics of the data. They either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We then train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. We simultaneously learn the graph edge importance within ST-GCN to enable interpretation of functional connectivities contributing to the prediction model. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N=1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. The matrix recording edge importance localizes brain regions and functional connections with significant aging and sex effects, which are verified by the neuroscience literature.