The estimation of sparse hierarchical components reflecting patterns of the
brain's functional connectivity from rsfMRI data can contribute to our
understanding of the brain's functional organization, and can lead to
biomarkers of diseases. However, inter-scanner variations and other confounding
factors pose a challenge to the robust and reproducible estimation of
functionally-interpretable brain networks, and especially to reproducible
biomarkers. Moreover, the brain is believed to be organized hierarchically, and
hence single-scale decompositions miss this hierarchy. The paper aims to use
current advancements in adversarial learning to estimate interpretable
hierarchical patterns in the human brain using rsfMRI data, which are robust to
"adversarial effects" such as inter-scanner variations. We write the estimation
problem as a minimization problem and solve it using alternating updates.
Extensive experiments on simulation and a real-world dataset show high
reproducibility of the components compared to other well-known methods.