STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC

TL;DR

This paper introduces STNAGNN, a novel graph neural network model that combines functional connectome data with data-driven spatio-temporal connections to better analyze brain fMRI data, overcoming limitations of traditional methods.

Contribution

The paper proposes STNAGNN, a new GNN model that integrates sparse FC with dense spatio-temporal data-driven connections for improved brain connectivity analysis.

Findings

Enhanced modeling of ROI interactions in fMRI data.

Improved accuracy over traditional FC-based methods.

Flexible spatio-temporal learning of brain connectivity patterns.

Abstract

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.