STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC
This work addresses the problem of oversimplified and noisy brain connectivity modeling in neuroscience for researchers using GNNs, representing an incremental improvement.
The paper tackled the challenge of defining brain connectivity in noisy fMRI data for graph neural networks, proposing STNAGNN as a data-driven method that combines sparse functional connectome with dense spatio-temporal connections, resulting in improved performance over baselines on classification tasks.
In recent years, graph neural networks (GNNs) have been widely applied in the analysis of brain fMRI, yet defining the connectivity between ROIs remains a challenge in noisy fMRI data. Among all approaches, Functional Connectome (FC) is the most popular method. Computed by the correlation coefficients between ROI time series, FC is a powerful and computationally efficient way to estimate ROI connectivity. However, it is well known for neglecting structural connections and causality in ROI interactions. Also, FC becomes much more noisy in the short spatio-temporal sliding-window subsequences of fMRI. Effective Connectome (EC) is proposed as a directional alternative, but is difficult to accurately estimate. Furthermore, for optimal GNN performance, usually only a small percentage of the strongest connections are selected as sparse edges, resulting in oversimplification of complex brain connections. To tackle these challenges, we propose the Spatio-Temporal Node Attention Graph Neural Network (STNAGNN) as a data-driven alternative that combines sparse predefined FC with dense data-driven spatio-temporal connections, allowing for flexible and spatio-temporal learning of ROI interaction patterns.