  We propose a hierarchical Bayesian recurrent state space model for modeling
switching network connectivity in resting state fMRI data. Our model allows us
to uncover shared network patterns across disease conditions. We evaluate our
method on the ADNI2 dataset by inferring latent state patterns corresponding to
altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In
addition to states shared across healthy and individuals with MCI, we discover
latent states that are predominantly observed in individuals with MCI. Our
model outperforms current state of the art deep learning method on ADNI2
dataset.
