Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers

Understand

How to identify and characterize functional brain networks (BN) is fundamental to gain system-level insights into the mechanisms of brain organizational architecture.

• Current functional magnetic resonance (fMRI) analysis highly relies on prior knowledge of specific patterns in either spatial (e.g., resting-state network) or temporal (e.g., task stimulus) domain.
• In addition, most approaches aim to find group-wise common functional networks, individual-specific functional networks have been rarely studied.
• In this work, we propose a novel Twin-Transformers framework to simultaneously infer common and individual functional networks in both spatial and temporal space, in a self-supervised manner.