  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. The first transformer takes space-divided information
as input and generates spatial features, while the second transformer takes
time-related information as input and outputs temporal features. The spatial
and temporal features are further separated into common and individual ones via
interactions (weights sharing) and constraints between the two transformers. We
applied our TwinTransformers to Human Connectome Project (HCP) motor task-fMRI
dataset and identified multiple common brain networks, including both
task-related and resting-state networks (e.g., default mode network).
Interestingly, we also successfully recovered a set of individual-specific
networks that are not related to task stimulus and only exist at the individual
level.
