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.
@article{arxiv.2011.07365,
title = {Bayesian recurrent state space model for rs-fMRI},
author = {Arunesh Mittal and Scott Linderman and John Paisley and Paul Sajda},
journal= {arXiv preprint arXiv:2011.07365},
year = {2020}
}
Comments
Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract