  Resting-state fMRI has been shown to provide surrogate biomarkers for the
analysis of various diseases. In addition, fMRI data helps in understanding the
brain's functional working during resting state and task-induced activity. To
improve the statistical power of biomarkers and the understanding mechanism of
the brain, pooling of multi-center studies has become increasingly popular. But
pooling the data from multiple sites introduces variations due to hardware,
software, and environment. In this paper, we look at the estimation problem of
hierarchical Sparsity Connectivity Patterns (hSCPs) in fMRI data acquired on
multiple sites. We introduce a simple yet effective matrix factorization based
formulation to reduce site-related effects while preserving biologically
relevant variations. We leverage adversarial learning in the unsupervised
regime to improve the reproducibility of the components. Experiments on
simulated datasets display that the proposed method can estimate components
with improved accuracy and reproducibility. We also demonstrate the improved
reproducibility of the components while preserving age-related variation on a
real dataset compiled from multiple sites.
