Spatio-Temporal Mapping Generative Adversarial Network for Functional Connectivity Network Reconstruction across Brain Atlases

Abstract

Functional connectivity networks (FCNs), as graph-structured data derived from functional magnetic resonance imaging (fMRI), are essential for understanding how brain functions coordinate with behavior and cognition. However, the utility of these FCNs is often limited by the brain atlas, since the predefined regions of interest by the atlas represent nodes in FCNs. To address these limitations and enhance the comparability of functional connectivity analyses across different atlases, we introduce the Spatio-Temporal Mapping Generative Adversarial Network (STMap-GAN) based on generative modeling. Convolutional networks and long short-term memory modules are used in the generator to improve the spatio and temporal consistency of generated fMRI time series for target brain atlases. The transformer module in the discriminator can effectively capture different features in fMRI time series, thus accurately distinguishing the generated time series from ground truth. This study demonstrates the ability of STMap-GAN to maintain high fidelity in FCN mapping across various atlases, ensuring consistency and replicability in neuroscience research.